Technologies for compressing communication for accelerator devices

ABSTRACT

Technologies for compressing communications for accelerator devices are disclosed. An accelerator device may include a communication abstraction logic units to manage communication with one or more remote accelerator devices. The communication abstraction logic unit may receive communication to and from a kernel on the accelerator device. The communication abstraction logic unit may compress and decompress the communication without instruction from the corresponding kernel. The communication abstraction logic unit may choose when and how to compress communications based on telemetry of the accelerator device and the remote accelerator device.

BACKGROUND

Accelerator devices, such as field programmable gate arrays (FPGAs), may be configured (e.g., by a bit stream defining a configuration of gates of the FPGA) to perform a set of functions, referred to herein as a kernel. The kernel may be configured, through time consuming effort on the part of a kernel developer, to establish a connection with another accelerator device kernel (e.g., another kernel in the same FPGA, a kernel in another FPGA, or a kernel of another type of accelerator device, such as a graphics processing unit (GPU), etc.) and share data with that other kernel to facilitate the completion of a workload (e.g., a set of operations that are to be performed). Typically, details of the communication protocol must be set out in the kernel by the kernel developer and, as the primary purpose of the kernel is to perform a set of operations (e.g., the workload) as efficiently as possible, the communication aspect of the kernel may be limited to communicating with a single type of accelerator device kernel if that accelerator device kernel happens to be available on a certain type of physical communication path (e.g., a PCIe bus). While some data centers may utilize pools of disaggregated resources (e.g., accelerator devices) available through various types of physical communication paths, also referred to I/O channels or communication channels, a given accelerator device kernel may be unable to access (e.g., cooperatively execute a workload with) a large percentage of those other accelerator devices present in the data center due to the limited communication faculties of the accelerator device kernel and inabilities to manage differences between the implementations of the various kernels (e.g., the different programming languages in which the kernels may defined) and the underlying architectures of the accelerator devices on which the kernels are implemented.

BRIEF DESCRIPTION OF THE DRAWINGS

The concepts described herein are illustrated by way of example and not by way of limitation in the accompanying figures. For simplicity and clarity of illustration, elements illustrated in the figures are not necessarily drawn to scale. Where considered appropriate, reference labels have been repeated among the figures to indicate corresponding or analogous elements.

FIG. 1 is a simplified diagram of at least one embodiment of a data center for executing workloads with disaggregated resources;

FIG. 2 is a simplified diagram of at least one embodiment of a pod that may be included in the data center of FIG. 1;

FIG. 3 is a perspective view of at least one embodiment of a rack that may be included in the pod of FIG. 2;

FIG. 4 is a side elevation view of the rack of FIG. 3;

FIG. 5 is a perspective view of the rack of FIG. 3 having a sled mounted therein;

FIG. 6 is a is a simplified block diagram of at least one embodiment of a top side of the sled of FIG. 5;

FIG. 7 is a simplified block diagram of at least one embodiment of a bottom side of the sled of FIG. 6;

FIG. 8 is a simplified block diagram of at least one embodiment of a compute sled usable in the data center of FIG. 1;

FIG. 9 is a top perspective view of at least one embodiment of the compute sled of FIG. 8;

FIG. 10 is a simplified block diagram of at least one embodiment of an accelerator sled usable in the data center of FIG. 1;

FIG. 11 is a top perspective view of at least one embodiment of the accelerator sled of FIG. 10;

FIG. 12 is a simplified block diagram of at least one embodiment of a storage sled usable in the data center of FIG. 1;

FIG. 13 is a top perspective view of at least one embodiment of the storage sled of FIG. 12;

FIG. 14 is a simplified block diagram of at least one embodiment of a memory sled usable in the data center of FIG. 1;

FIG. 15 is a simplified block diagram of a system that may be established within the data center of FIG. 1 to execute workloads with managed nodes composed of disaggregated resources;

FIG. 16 is a simplified diagram of at least one embodiment of a system for providing communication abstraction for accelerator device kernels to support scale-up and scale-out;

FIGS. 17-20 are a simplified block diagram of at least one embodiment of a method for providing communication abstraction for an accelerator device kernel that may be performed by an accelerator device included in the system of FIG. 16 to support scale-up and/or scale-out;

FIG. 21 is a simplified block diagram of at least one embodiment of a method for compressing and decompressing communication between an accelerator device and a compute device; and

FIG. 22 is a simplified block diagram of at least one embodiment of a method for compressing and decompressing communication from and to an accelerator device.

DETAILED DESCRIPTION OF THE DRAWINGS

While the concepts of the present disclosure are susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and will be described herein in detail. It should be understood, however, that there is no intent to limit the concepts of the present disclosure to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives consistent with the present disclosure and the appended claims.

References in the specification to “one embodiment,” “an embodiment,” “an illustrative embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may or may not necessarily include that particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. Additionally, it should be appreciated that items included in a list in the form of “at least one A, B, and C” can mean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C). Similarly, items listed in the form of “at least one of A, B, or C” can mean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).

The disclosed embodiments may be implemented, in some cases, in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on a transitory or non-transitory machine-readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors. A machine-readable storage medium may be embodied as any storage device, mechanism, or other physical structure for storing or transmitting information in a form readable by a machine (e.g., a volatile or non-volatile memory, a media disc, or other media device).

In the drawings, some structural or method features may be shown in specific arrangements and/or orderings. However, it should be appreciated that such specific arrangements and/or orderings may not be required. Rather, in some embodiments, such features may be arranged in a different manner and/or order than shown in the illustrative figures. Additionally, the inclusion of a structural or method feature in a particular figure is not meant to imply that such feature is required in all embodiments and, in some embodiments, may not be included or may be combined with other features.

Referring now to FIG. 1, a data center 100 in which disaggregated resources may cooperatively execute one or more workloads (e.g., applications on behalf of customers) includes multiple pods 110, 120, 130, 140, each of which includes one or more rows of racks. Of course, although data center 100 is shown with multiple pods, in some embodiments, the data center 100 may be embodied as a single pod. As described in more detail herein, each rack houses multiple sleds, each of which may be primarily equipped with a particular type of resource (e.g., memory devices, data storage devices, accelerator devices, general purpose processors), i.e., resources that can be logically coupled to form a composed node, which can act as, for example, a server. In the illustrative embodiment, the sleds in each pod 110, 120, 130, 140 are connected to multiple pod switches (e.g., switches that route data communications to and from sleds within the pod). The pod switches, in turn, connect with spine switches 150 that switch communications among pods (e.g., the pods 110, 120, 130, 140) in the data center 100. In some embodiments, the sleds may be connected with a fabric using Intel Omni-Path technology. In other embodiments, the sleds may be connected with other fabrics, such as InfiniBand or Ethernet. As described in more detail herein, resources within sleds in the data center 100 may be allocated to a group (referred to herein as a “managed node”) containing resources from one or more sleds to be collectively utilized in the execution of a workload. The workload can execute as if the resources belonging to the managed node were located on the same sled. The resources in a managed node may belong to sleds belonging to different racks, and even to different pods 110, 120, 130, 140. As such, some resources of a single sled may be allocated to one managed node while other resources of the same sled are allocated to a different managed node (e.g., one processor assigned to one managed node and another processor of the same sled assigned to a different managed node).

A data center comprising disaggregated resources, such as data center 100, can be used in a wide variety of contexts, such as enterprise, government, cloud service provider, and communications service provider (e.g., Telco's), as well in a wide variety of sizes, from cloud service provider mega-data centers that consume over 100,000 sq. ft. to single- or multi-rack installations for use in base stations.

The disaggregation of resources to sleds comprised predominantly of a single type of resource (e.g., compute sleds comprising primarily compute resources, memory sleds containing primarily memory resources), and the selective allocation and deallocation of the disaggregated resources to form a managed node assigned to execute a workload improves the operation and resource usage of the data center 100 relative to typical data centers comprised of hyperconverged servers containing compute, memory, storage and perhaps additional resources in a single chassis. For example, because sleds predominantly contain resources of a particular type, resources of a given type can be upgraded independently of other resources. Additionally, because different resources types (processors, storage, accelerators, etc.) typically have different refresh rates, greater resource utilization and reduced total cost of ownership may be achieved. For example, a data center operator can upgrade the processors throughout their facility by only swapping out the compute sleds. In such a case, accelerator and storage resources may not be contemporaneously upgraded and, rather, may be allowed to continue operating until those resources are scheduled for their own refresh. Resource utilization may also increase. For example, if managed nodes are composed based on requirements of the workloads that will be running on them, resources within a node are more likely to be fully utilized. Such utilization may allow for more managed nodes to run in a data center with a given set of resources, or for a data center expected to run a given set of workloads, to be built using fewer resources.

Referring now to FIG. 2, the pod 110, in the illustrative embodiment, includes a set of rows 200, 210, 220, 230 of racks 240. Each rack 240 may house multiple sleds (e.g., sixteen sleds) and provide power and data connections to the housed sleds, as described in more detail herein. In the illustrative embodiment, the racks in each row 200, 210, 220, 230 are connected to multiple pod switches 250, 260. The pod switch 250 includes a set of ports 252 to which the sleds of the racks of the pod 110 are connected and another set of ports 254 that connect the pod 110 to the spine switches 150 to provide connectivity to other pods in the data center 100. Similarly, the pod switch 260 includes a set of ports 262 to which the sleds of the racks of the pod 110 are connected and a set of ports 264 that connect the pod 110 to the spine switches 150. As such, the use of the pair of switches 250, 260 provides an amount of redundancy to the pod 110. For example, if either of the switches 250, 260 fails, the sleds in the pod 110 may still maintain data communication with the remainder of the data center 100 (e.g., sleds of other pods) through the other switch 250, 260. Furthermore, in the illustrative embodiment, the switches 150, 250, 260 may be embodied as dual-mode optical switches, capable of routing both Ethernet protocol communications carrying Internet Protocol (IP) packets and communications according to a second, high-performance link-layer protocol (e.g., Intel's Omni-Path Architecture's, InfiniBand, PCI Express) via optical signaling media of an optical fabric.

It should be appreciated that each of the other pods 120, 130, 140 (as well as any additional pods of the data center 100) may be similarly structured as, and have components similar to, the pod 110 shown in and described in regard to FIG. 2 (e.g., each pod may have rows of racks housing multiple sleds as described above). Additionally, while two pod switches 250, 260 are shown, it should be understood that in other embodiments, each pod 110, 120, 130, 140 may be connected to a different number of pod switches, providing even more failover capacity. Of course, in other embodiments, pods may be arranged differently than the rows-of-racks configuration shown in FIGS. 1-2. For example, a pod may be embodied as multiple sets of racks in which each set of racks is arranged radially, i.e., the racks are equidistant from a center switch.

Referring now to FIGS. 3-5, each illustrative rack 240 of the data center 100 includes two elongated support posts 302, 304, which are arranged vertically. For example, the elongated support posts 302, 304 may extend upwardly from a floor of the data center 100 when deployed. The rack 240 also includes one or more horizontal pairs 310 of elongated support arms 312 (identified in FIG. 3 via a dashed ellipse) configured to support a sled of the data center 100 as discussed below. One elongated support arm 312 of the pair of elongated support arms 312 extends outwardly from the elongated support post 302 and the other elongated support arm 312 extends outwardly from the elongated support post 304.

In the illustrative embodiments, each sled of the data center 100 is embodied as a chassis-less sled. That is, each sled has a chassis-less circuit board substrate on which physical resources (e.g., processors, memory, accelerators, storage, etc.) are mounted as discussed in more detail below. As such, the rack 240 is configured to receive the chassis-less sleds. For example, each pair 310 of elongated support arms 312 defines a sled slot 320 of the rack 240, which is configured to receive a corresponding chassis-less sled. To do so, each illustrative elongated support arm 312 includes a circuit board guide 330 configured to receive the chassis-less circuit board substrate of the sled. Each circuit board guide 330 is secured to, or otherwise mounted to, a top side 332 of the corresponding elongated support arm 312. For example, in the illustrative embodiment, each circuit board guide 330 is mounted at a distal end of the corresponding elongated support arm 312 relative to the corresponding elongated support post 302, 304. For clarity of the Figures, not every circuit board guide 330 may be referenced in each Figure.

Each circuit board guide 330 includes an inner wall that defines a circuit board slot 380 configured to receive the chassis-less circuit board substrate of a sled 400 when the sled 400 is received in the corresponding sled slot 320 of the rack 240. To do so, as shown in FIG. 4, a user (or robot) aligns the chassis-less circuit board substrate of an illustrative chassis-less sled 400 to a sled slot 320. The user, or robot, may then slide the chassis-less circuit board substrate forward into the sled slot 320 such that each side edge 414 of the chassis-less circuit board substrate is received in a corresponding circuit board slot 380 of the circuit board guides 330 of the pair 310 of elongated support arms 312 that define the corresponding sled slot 320 as shown in FIG. 4. By having robotically accessible and robotically manipulable sleds comprising disaggregated resources, each type of resource can be upgraded independently of each other and at their own optimized refresh rate. Furthermore, the sleds are configured to blindly mate with power and data communication cables in each rack 240, enhancing their ability to be quickly removed, upgraded, reinstalled, and/or replaced. As such, in some embodiments, the data center 100 may operate (e.g., execute workloads, undergo maintenance and/or upgrades, etc.) without human involvement on the data center floor. In other embodiments, a human may facilitate one or more maintenance or upgrade operations in the data center 100.

It should be appreciated that each circuit board guide 330 is dual sided. That is, each circuit board guide 330 includes an inner wall that defines a circuit board slot 380 on each side of the circuit board guide 330. In this way, each circuit board guide 330 can support a chassis-less circuit board substrate on either side. As such, a single additional elongated support post may be added to the rack 240 to turn the rack 240 into a two-rack solution that can hold twice as many sled slots 320 as shown in FIG. 3. The illustrative rack 240 includes seven pairs 310 of elongated support arms 312 that define a corresponding seven sled slots 320, each configured to receive and support a corresponding sled 400 as discussed above. Of course, in other embodiments, the rack 240 may include additional or fewer pairs 310 of elongated support arms 312 (i.e., additional or fewer sled slots 320). It should be appreciated that because the sled 400 is chassis-less, the sled 400 may have an overall height that is different than typical servers. As such, in some embodiments, the height of each sled slot 320 may be shorter than the height of a typical server (e.g., shorter than a single rank unit, “1U”). That is, the vertical distance between each pair 310 of elongated support arms 312 may be less than a standard rack unit “1U.” Additionally, due to the relative decrease in height of the sled slots 320, the overall height of the rack 240 in some embodiments may be shorter than the height of traditional rack enclosures. For example, in some embodiments, each of the elongated support posts 302, 304 may have a length of six feet or less. Again, in other embodiments, the rack 240 may have different dimensions. For example, in some embodiments, the vertical distance between each pair 310 of elongated support arms 312 may be greater than a standard rack until “1U”. In such embodiments, the increased vertical distance between the sleds allows for larger heat sinks to be attached to the physical resources and for larger fans to be used (e.g., in the fan array 370 described below) for cooling each sled, which in turn can allow the physical resources to operate at increased power levels. Further, it should be appreciated that the rack 240 does not include any walls, enclosures, or the like. Rather, the rack 240 is an enclosure-less rack that is opened to the local environment. Of course, in some cases, an end plate may be attached to one of the elongated support posts 302, 304 in those situations in which the rack 240 forms an end-of-row rack in the data center 100.

In some embodiments, various interconnects may be routed upwardly or downwardly through the elongated support posts 302, 304. To facilitate such routing, each elongated support post 302, 304 includes an inner wall that defines an inner chamber in which interconnects may be located. The interconnects routed through the elongated support posts 302, 304 may be embodied as any type of interconnects including, but not limited to, data or communication interconnects to provide communication connections to each sled slot 320, power interconnects to provide power to each sled slot 320, and/or other types of interconnects.

The rack 240, in the illustrative embodiment, includes a support platform on which a corresponding optical data connector (not shown) is mounted. Each optical data connector is associated with a corresponding sled slot 320 and is configured to mate with an optical data connector of a corresponding sled 400 when the sled 400 is received in the corresponding sled slot 320. In some embodiments, optical connections between components (e.g., sleds, racks, and switches) in the data center 100 are made with a blind mate optical connection. For example, a door on each cable may prevent dust from contaminating the fiber inside the cable. In the process of connecting to a blind mate optical connector mechanism, the door is pushed open when the end of the cable approaches or enters the connector mechanism. Subsequently, the optical fiber inside the cable may enter a gel within the connector mechanism and the optical fiber of one cable comes into contact with the optical fiber of another cable within the gel inside the connector mechanism.

The illustrative rack 240 also includes a fan array 370 coupled to the cross-support arms of the rack 240. The fan array 370 includes one or more rows of cooling fans 372, which are aligned in a horizontal line between the elongated support posts 302, 304. In the illustrative embodiment, the fan array 370 includes a row of cooling fans 372 for each sled slot 320 of the rack 240. As discussed above, each sled 400 does not include any on-board cooling system in the illustrative embodiment and, as such, the fan array 370 provides cooling for each sled 400 received in the rack 240. Each rack 240, in the illustrative embodiment, also includes a power supply associated with each sled slot 320. Each power supply is secured to one of the elongated support arms 312 of the pair 310 of elongated support arms 312 that define the corresponding sled slot 320. For example, the rack 240 may include a power supply coupled or secured to each elongated support arm 312 extending from the elongated support post 302. Each power supply includes a power connector configured to mate with a power connector of the sled 400 when the sled 400 is received in the corresponding sled slot 320. In the illustrative embodiment, the sled 400 does not include any on-board power supply and, as such, the power supplies provided in the rack 240 supply power to corresponding sleds 400 when mounted to the rack 240. Each power supply is configured to satisfy the power requirements for its associated sled, which can vary from sled to sled. Additionally, the power supplies provided in the rack 240 can operate independent of each other. That is, within a single rack, a first power supply providing power to a compute sled can provide power levels that are different than power levels supplied by a second power supply providing power to an accelerator sled. The power supplies may be controllable at the sled level or rack level, and may be controlled locally by components on the associated sled or remotely, such as by another sled or an orchestrator.

Referring now to FIG. 6, the sled 400, in the illustrative embodiment, is configured to be mounted in a corresponding rack 240 of the data center 100 as discussed above. In some embodiments, each sled 400 may be optimized or otherwise configured for performing particular tasks, such as compute tasks, acceleration tasks, data storage tasks, etc. For example, the sled 400 may be embodied as a compute sled 800 as discussed below in regard to FIGS. 8-9, an accelerator sled 1000 as discussed below in regard to FIGS. 10-11, a storage sled 1200 as discussed below in regard to FIGS. 12-13, or as a sled optimized or otherwise configured to perform other specialized tasks, such as a memory sled 1400, discussed below in regard to FIG. 14.

As discussed above, the illustrative sled 400 includes a chassis-less circuit board substrate 602, which supports various physical resources (e.g., electrical components) mounted thereon. It should be appreciated that the circuit board substrate 602 is “chassis-less” in that the sled 400 does not include a housing or enclosure. Rather, the chassis-less circuit board substrate 602 is open to the local environment. The chassis-less circuit board substrate 602 may be formed from any material capable of supporting the various electrical components mounted thereon. For example, in an illustrative embodiment, the chassis-less circuit board substrate 602 is formed from an FR-4 glass-reinforced epoxy laminate material. Of course, other materials may be used to form the chassis-less circuit board substrate 602 in other embodiments.

As discussed in more detail below, the chassis-less circuit board substrate 602 includes multiple features that improve the thermal cooling characteristics of the various electrical components mounted on the chassis-less circuit board substrate 602. As discussed, the chassis-less circuit board substrate 602 does not include a housing or enclosure, which may improve the airflow over the electrical components of the sled 400 by reducing those structures that may inhibit air flow. For example, because the chassis-less circuit board substrate 602 is not positioned in an individual housing or enclosure, there is no vertically-arranged backplane (e.g., a backplate of the chassis) attached to the chassis-less circuit board substrate 602, which could inhibit air flow across the electrical components. Additionally, the chassis-less circuit board substrate 602 has a geometric shape configured to reduce the length of the airflow path across the electrical components mounted to the chassis-less circuit board substrate 602. For example, the illustrative chassis-less circuit board substrate 602 has a width 604 that is greater than a depth 606 of the chassis-less circuit board substrate 602. In one particular embodiment, for example, the chassis-less circuit board substrate 602 has a width of about 21 inches and a depth of about 9 inches, compared to a typical server that has a width of about 17 inches and a depth of about 39 inches. As such, an airflow path 608 that extends from a front edge 610 of the chassis-less circuit board substrate 602 toward a rear edge 612 has a shorter distance relative to typical servers, which may improve the thermal cooling characteristics of the sled 400. Furthermore, although not illustrated in FIG. 6, the various physical resources mounted to the chassis-less circuit board substrate 602 are mounted in corresponding locations such that no two substantively heat-producing electrical components shadow each other as discussed in more detail below. That is, no two electrical components, which produce appreciable heat during operation (i.e., greater than a nominal heat sufficient enough to adversely impact the cooling of another electrical component), are mounted to the chassis-less circuit board substrate 602 linearly in-line with each other along the direction of the airflow path 608 (i.e., along a direction extending from the front edge 610 toward the rear edge 612 of the chassis-less circuit board substrate 602).

As discussed above, the illustrative sled 400 includes one or more physical resources 620 mounted to a top side 650 of the chassis-less circuit board substrate 602. Although two physical resources 620 are shown in FIG. 6, it should be appreciated that the sled 400 may include one, two, or more physical resources 620 in other embodiments. The physical resources 620 may be embodied as any type of processor, controller, or other compute circuit capable of performing various tasks such as compute functions and/or controlling the functions of the sled 400 depending on, for example, the type or intended functionality of the sled 400. For example, as discussed in more detail below, the physical resources 620 may be embodied as high-performance processors in embodiments in which the sled 400 is embodied as a compute sled, as accelerator co-processors or circuits in embodiments in which the sled 400 is embodied as an accelerator sled, storage controllers in embodiments in which the sled 400 is embodied as a storage sled, or a set of memory devices in embodiments in which the sled 400 is embodied as a memory sled.

The sled 400 also includes one or more additional physical resources 630 mounted to the top side 650 of the chassis-less circuit board substrate 602. In the illustrative embodiment, the additional physical resources include a network interface controller (NIC) as discussed in more detail below. Of course, depending on the type and functionality of the sled 400, the physical resources 630 may include additional or other electrical components, circuits, and/or devices in other embodiments.

The physical resources 620 are communicatively coupled to the physical resources 630 via an input/output (I/O) subsystem 622. The I/O subsystem 622 may be embodied as circuitry and/or components to facilitate input/output operations with the physical resources 620, the physical resources 630, and/or other components of the sled 400. For example, the I/O subsystem 622 may be embodied as, or otherwise include, memory controller hubs, input/output control hubs, integrated sensor hubs, firmware devices, communication links (e.g., point-to-point links, bus links, wires, cables, waveguides, light guides, printed circuit board traces, etc.), and/or other components and subsystems to facilitate the input/output operations. In the illustrative embodiment, the I/O subsystem 622 is embodied as, or otherwise includes, a double data rate 4 (DDR4) data bus or a DDR5 data bus.

In some embodiments, the sled 400 may also include a resource-to-resource interconnect 624. The resource-to-resource interconnect 624 may be embodied as any type of communication interconnect capable of facilitating resource-to-resource communications. In the illustrative embodiment, the resource-to-resource interconnect 624 is embodied as a high-speed point-to-point interconnect (e.g., faster than the I/O subsystem 622). For example, the resource-to-resource interconnect 624 may be embodied as a QuickPath Interconnect (QPI), an UltraPath Interconnect (UPI), or other high-speed point-to-point interconnect dedicated to resource-to-resource communications.

The sled 400 also includes a power connector 640 configured to mate with a corresponding power connector of the rack 240 when the sled 400 is mounted in the corresponding rack 240. The sled 400 receives power from a power supply of the rack 240 via the power connector 640 to supply power to the various electrical components of the sled 400. That is, the sled 400 does not include any local power supply (i.e., an on-board power supply) to provide power to the electrical components of the sled 400. The exclusion of a local or on-board power supply facilitates the reduction in the overall footprint of the chassis-less circuit board substrate 602, which may increase the thermal cooling characteristics of the various electrical components mounted on the chassis-less circuit board substrate 602 as discussed above. In some embodiments, voltage regulators are placed on a bottom side 750 (see FIG. 7) of the chassis-less circuit board substrate 602 directly opposite of the processors 820 (see FIG. 8), and power is routed from the voltage regulators to the processors 820 by vias extending through the circuit board substrate 602. Such a configuration provides an increased thermal budget, additional current and/or voltage, and better voltage control relative to typical printed circuit boards in which processor power is delivered from a voltage regulator, in part, by printed circuit traces.

In some embodiments, the sled 400 may also include mounting features 642 configured to mate with a mounting arm, or other structure, of a robot to facilitate the placement of the sled 600 in a rack 240 by the robot. The mounting features 642 may be embodied as any type of physical structures that allow the robot to grasp the sled 400 without damaging the chassis-less circuit board substrate 602 or the electrical components mounted thereto. For example, in some embodiments, the mounting features 642 may be embodied as non-conductive pads attached to the chassis-less circuit board substrate 602. In other embodiments, the mounting features may be embodied as brackets, braces, or other similar structures attached to the chassis-less circuit board substrate 602. The particular number, shape, size, and/or make-up of the mounting feature 642 may depend on the design of the robot configured to manage the sled 400.

Referring now to FIG. 7, in addition to the physical resources 630 mounted on the top side 650 of the chassis-less circuit board substrate 602, the sled 400 also includes one or more memory devices 720 mounted to a bottom side 750 of the chassis-less circuit board substrate 602. That is, the chassis-less circuit board substrate 602 is embodied as a double-sided circuit board. The physical resources 620 are communicatively coupled to the memory devices 720 via the I/O subsystem 622. For example, the physical resources 620 and the memory devices 720 may be communicatively coupled by one or more vias extending through the chassis-less circuit board substrate 602. Each physical resource 620 may be communicatively coupled to a different set of one or more memory devices 720 in some embodiments.

Alternatively, in other embodiments, each physical resource 620 may be communicatively coupled to each memory device 720.

The memory devices 720 may be embodied as any type of memory device capable of storing data for the physical resources 620 during operation of the sled 400, such as any type of volatile (e.g., dynamic random access memory (DRAM), etc.) or non-volatile memory. Volatile memory may be a storage medium that requires power to maintain the state of data stored by the medium. Non-limiting examples of volatile memory may include various types of random access memory (RAM), such as dynamic random access memory (DRAM) or static random access memory (SRAM). One particular type of DRAM that may be used in a memory module is synchronous dynamic random access memory (SDRAM). In particular embodiments, DRAM of a memory component may comply with a standard promulgated by JEDEC, such as JESD79F for DDR SDRAM, JESD79-2F for DDR2 SDRAM, JESD79-3F for DDR3 SDRAM, JESD79-4A for DDR4 SDRAM, JESD209 for Low Power DDR (LPDDR), JESD209-2 for LPDDR2, JESD209-3 for LPDDR3, and JESD209-4 for LPDDR4. Such standards (and similar standards) may be referred to as DDR-based standards and communication interfaces of the storage devices that implement such standards may be referred to as DDR-based interfaces.

In one embodiment, the memory device is a block addressable memory device, such as those based on NAND or NOR technologies. A memory device may also include next-generation nonvolatile devices, such as Intel 3D XPoint™ memory or other byte addressable write-in-place nonvolatile memory devices. In one embodiment, the memory device may be or may include memory devices that use chalcogenide glass, multi-threshold level NAND flash memory, NOR flash memory, single or multi-level Phase Change Memory (PCM), a resistive memory, nanowire memory, ferroelectric transistor random access memory (FeTRAM), anti-ferroelectric memory, magnetoresistive random access memory (MRAM) memory that incorporates memristor technology, resistive memory including the metal oxide base, the oxygen vacancy base and the conductive bridge Random Access Memory (CB-RAM), or spin transfer torque (STT)-MRAM, a spintronic magnetic junction memory based device, a magnetic tunneling junction (MTJ) based device, a DW (Domain Wall) and SOT (Spin Orbit Transfer) based device, a thyristor based memory device, or a combination of any of the above, or other memory. The memory device may refer to the die itself and/or to a packaged memory product. In some embodiments, the memory device may comprise a transistor-less stackable cross point architecture in which memory cells sit at the intersection of word lines and bit lines and are individually addressable and in which bit storage is based on a change in bulk resistance.

Referring now to FIG. 8, in some embodiments, the sled 400 may be embodied as a compute sled 800. The compute sled 800 is optimized, or otherwise configured, to perform compute tasks. Of course, as discussed above, the compute sled 800 may rely on other sleds, such as acceleration sleds and/or storage sleds, to perform such compute tasks. The compute sled 800 includes various physical resources (e.g., electrical components) similar to the physical resources of the sled 400, which have been identified in FIG. 8 using the same reference numbers. The description of such components provided above in regard to FIGS. 6 and 7 applies to the corresponding components of the compute sled 800 and is not repeated herein for clarity of the description of the compute sled 800.

In the illustrative compute sled 800, the physical resources 620 are embodied as processors 820. Although only two processors 820 are shown in FIG. 8, it should be appreciated that the compute sled 800 may include additional processors 820 in other embodiments. Illustratively, the processors 820 are embodied as high-performance processors 820 and may be configured to operate at a relatively high power rating. Although the processors 820 generate additional heat operating at power ratings greater than typical processors (which operate at around 155-230 W), the enhanced thermal cooling characteristics of the chassis-less circuit board substrate 602 discussed above facilitate the higher power operation. For example, in the illustrative embodiment, the processors 820 are configured to operate at a power rating of at least 250 W. In some embodiments, the processors 820 may be configured to operate at a power rating of at least 350 W.

In some embodiments, the compute sled 800 may also include a processor-to-processor interconnect 842. Similar to the resource-to-resource interconnect 624 of the sled 400 discussed above, the processor-to-processor interconnect 842 may be embodied as any type of communication interconnect capable of facilitating processor-to-processor interconnect 842 communications. In the illustrative embodiment, the processor-to-processor interconnect 842 is embodied as a high-speed point-to-point interconnect (e.g., faster than the I/O subsystem 622). For example, the processor-to-processor interconnect 842 may be embodied as a QuickPath Interconnect (QPI), an UltraPath Interconnect (UPI), or other high-speed point-to-point interconnect dedicated to processor-to-processor communications.

The compute sled 800 also includes a communication circuit 830. The illustrative communication circuit 830 includes a network interface controller (NIC) 832, which may also be referred to as a host fabric interface (HFI). The NIC 832 may be embodied as, or otherwise include, any type of integrated circuit, discrete circuits, controller chips, chipsets, add-in-boards, daughtercards, network interface cards, or other devices that may be used by the compute sled 800 to connect with another compute device (e.g., with other sleds 400). In some embodiments, the NIC 832 may be embodied as part of a system-on-a-chip (SoC) that includes one or more processors, or included on a multichip package that also contains one or more processors. In some embodiments, the NIC 832 may include a local processor (not shown) and/or a local memory (not shown) that are both local to the NIC 832. In such embodiments, the local processor of the NIC 832 may be capable of performing one or more of the functions of the processors 820. Additionally or alternatively, in such embodiments, the local memory of the NIC 832 may be integrated into one or more components of the compute sled at the board level, socket level, chip level, and/or other levels.

The communication circuit 830 is communicatively coupled to an optical data connector 834. The optical data connector 834 is configured to mate with a corresponding optical data connector of the rack 240 when the compute sled 800 is mounted in the rack 240. Illustratively, the optical data connector 834 includes a plurality of optical fibers which lead from a mating surface of the optical data connector 834 to an optical transceiver 836. The optical transceiver 836 is configured to convert incoming optical signals from the rack-side optical data connector to electrical signals and to convert electrical signals to outgoing optical signals to the rack-side optical data connector. Although shown as forming part of the optical data connector 834 in the illustrative embodiment, the optical transceiver 836 may form a portion of the communication circuit 830 in other embodiments.

In some embodiments, the compute sled 800 may also include an expansion connector 840. In such embodiments, the expansion connector 840 is configured to mate with a corresponding connector of an expansion chassis-less circuit board substrate to provide additional physical resources to the compute sled 800. The additional physical resources may be used, for example, by the processors 820 during operation of the compute sled 800. The expansion chassis-less circuit board substrate may be substantially similar to the chassis-less circuit board substrate 602 discussed above and may include various electrical components mounted thereto. The particular electrical components mounted to the expansion chassis-less circuit board substrate may depend on the intended functionality of the expansion chassis-less circuit board substrate. For example, the expansion chassis-less circuit board substrate may provide additional compute resources, memory resources, and/or storage resources. As such, the additional physical resources of the expansion chassis-less circuit board substrate may include, but is not limited to, processors, memory devices, storage devices, and/or accelerator circuits including, for example, field programmable gate arrays (FPGA), application-specific integrated circuits (ASICs), security co-processors, graphics processing units (GPUs), machine learning circuits, or other specialized processors, controllers, devices, and/or circuits.

Referring now to FIG. 9, an illustrative embodiment of the compute sled 800 is shown. As shown, the processors 820, communication circuit 830, and optical data connector 834 are mounted to the top side 650 of the chassis-less circuit board substrate 602. Any suitable attachment or mounting technology may be used to mount the physical resources of the compute sled 800 to the chassis-less circuit board substrate 602. For example, the various physical resources may be mounted in corresponding sockets (e.g., a processor socket), holders, or brackets. In some cases, some of the electrical components may be directly mounted to the chassis-less circuit board substrate 602 via soldering or similar techniques.

As discussed above, the individual processors 820 and communication circuit 830 are mounted to the top side 650 of the chassis-less circuit board substrate 602 such that no two heat-producing, electrical components shadow each other. In the illustrative embodiment, the processors 820 and communication circuit 830 are mounted in corresponding locations on the top side 650 of the chassis-less circuit board substrate 602 such that no two of those physical resources are linearly in-line with others along the direction of the airflow path 608. It should be appreciated that, although the optical data connector 834 is in-line with the communication circuit 830, the optical data connector 834 produces no or nominal heat during operation.

The memory devices 720 of the compute sled 800 are mounted to the bottom side 750 of the of the chassis-less circuit board substrate 602 as discussed above in regard to the sled 400. Although mounted to the bottom side 750, the memory devices 720 are communicatively coupled to the processors 820 located on the top side 650 via the I/O subsystem 622. Because the chassis-less circuit board substrate 602 is embodied as a double-sided circuit board, the memory devices 720 and the processors 820 may be communicatively coupled by one or more vias, connectors, or other mechanisms extending through the chassis-less circuit board substrate 602. Of course, each processor 820 may be communicatively coupled to a different set of one or more memory devices 720 in some embodiments. Alternatively, in other embodiments, each processor 820 may be communicatively coupled to each memory device 720. In some embodiments, the memory devices 720 may be mounted to one or more memory mezzanines on the bottom side of the chassis-less circuit board substrate 602 and may interconnect with a corresponding processor 820 through a ball-grid array.

Each of the processors 820 includes a heatsink 850 secured thereto. Due to the mounting of the memory devices 720 to the bottom side 750 of the chassis-less circuit board substrate 602 (as well as the vertical spacing of the sleds 400 in the corresponding rack 240), the top side 650 of the chassis-less circuit board substrate 602 includes additional “free” area or space that facilitates the use of heatsinks 850 having a larger size relative to traditional heatsinks used in typical servers. Additionally, due to the improved thermal cooling characteristics of the chassis-less circuit board substrate 602, none of the processor heatsinks 850 include cooling fans attached thereto. That is, each of the heatsinks 850 is embodied as a fan-less heatsink. In some embodiments, the heat sinks 850 mounted atop the processors 820 may overlap with the heat sink attached to the communication circuit 830 in the direction of the airflow path 608 due to their increased size, as illustratively suggested by FIG. 9.

Referring now to FIG. 10, in some embodiments, the sled 400 may be embodied as an accelerator sled 1000. The accelerator sled 1000 is configured, to perform specialized compute tasks, such as machine learning, encryption, hashing, or other computational-intensive task. In some embodiments, for example, a compute sled 800 may offload tasks to the accelerator sled 1000 during operation. The accelerator sled 1000 includes various components similar to components of the sled 400 and/or compute sled 800, which have been identified in FIG. 10 using the same reference numbers. The description of such components provided above in regard to FIGS. 6, 7, and 8 apply to the corresponding components of the accelerator sled 1000 and is not repeated herein for clarity of the description of the accelerator sled 1000.

In the illustrative accelerator sled 1000, the physical resources 620 are embodied as accelerator circuits 1020. Although only two accelerator circuits 1020 are shown in FIG. 10, it should be appreciated that the accelerator sled 1000 may include additional accelerator circuits 1020 in other embodiments. For example, as shown in FIG. 11, the accelerator sled 1000 may include four accelerator circuits 1020 in some embodiments. The accelerator circuits 1020 may be embodied as any type of processor, co-processor, compute circuit, or other device capable of performing compute or processing operations. For example, the accelerator circuits 1020 may be embodied as, for example, field programmable gate arrays (FPGA), application-specific integrated circuits (ASICs), security co-processors, graphics processing units (GPUs), neuromorphic processor units, quantum computers, machine learning circuits, or other specialized processors, controllers, devices, and/or circuits.

In some embodiments, the accelerator sled 1000 may also include an accelerator-to-accelerator interconnect 1042. Similar to the resource-to-resource interconnect 624 of the sled 600 discussed above, the accelerator-to-accelerator interconnect 1042 may be embodied as any type of communication interconnect capable of facilitating accelerator-to-accelerator communications. In the illustrative embodiment, the accelerator-to-accelerator interconnect 1042 is embodied as a high-speed point-to-point interconnect (e.g., faster than the I/O subsystem 622). For example, the accelerator-to-accelerator interconnect 1042 may be embodied as a QuickPath Interconnect (QPI), an UltraPath Interconnect (UPI), or other high-speed point-to-point interconnect dedicated to processor-to-processor communications. In some embodiments, the accelerator circuits 1020 may be daisy-chained with a primary accelerator circuit 1020 connected to the NIC 832 and memory 720 through the I/O subsystem 622 and a secondary accelerator circuit 1020 connected to the NIC 832 and memory 720 through a primary accelerator circuit 1020.

Referring now to FIG. 11, an illustrative embodiment of the accelerator sled 1000 is shown. As discussed above, the accelerator circuits 1020, communication circuit 830, and optical data connector 834 are mounted to the top side 650 of the chassis-less circuit board substrate 602. Again, the individual accelerator circuits 1020 and communication circuit 830 are mounted to the top side 650 of the chassis-less circuit board substrate 602 such that no two heat-producing, electrical components shadow each other as discussed above. The memory devices 720 of the accelerator sled 1000 are mounted to the bottom side 750 of the of the chassis-less circuit board substrate 602 as discussed above in regard to the sled 600. Although mounted to the bottom side 750, the memory devices 720 are communicatively coupled to the accelerator circuits 1020 located on the top side 650 via the I/O subsystem 622 (e.g., through vias). Further, each of the accelerator circuits 1020 may include a heatsink 1070 that is larger than a traditional heatsink used in a server. As discussed above with reference to the heatsinks 870, the heatsinks 1070 may be larger than traditional heatsinks because of the “free” area provided by the memory resources 720 being located on the bottom side 750 of the chassis-less circuit board substrate 602 rather than on the top side 650.

Referring now to FIG. 12, in some embodiments, the sled 400 may be embodied as a storage sled 1200. The storage sled 1200 is configured, to store data in a data storage 1250 local to the storage sled 1200. For example, during operation, a compute sled 800 or an accelerator sled 1000 may store and retrieve data from the data storage 1250 of the storage sled 1200. The storage sled 1200 includes various components similar to components of the sled 400 and/or the compute sled 800, which have been identified in FIG. 12 using the same reference numbers. The description of such components provided above in regard to FIGS. 6, 7, and 8 apply to the corresponding components of the storage sled 1200 and is not repeated herein for clarity of the description of the storage sled 1200.

In the illustrative storage sled 1200, the physical resources 620 are embodied as storage controllers 1220. Although only two storage controllers 1220 are shown in FIG. 12, it should be appreciated that the storage sled 1200 may include additional storage controllers 1220 in other embodiments. The storage controllers 1220 may be embodied as any type of processor, controller, or control circuit capable of controlling the storage and retrieval of data into the data storage 1250 based on requests received via the communication circuit 830. In the illustrative embodiment, the storage controllers 1220 are embodied as relatively low-power processors or controllers. For example, in some embodiments, the storage controllers 1220 may be configured to operate at a power rating of about 75 watts.

In some embodiments, the storage sled 1200 may also include a controller-to-controller interconnect 1242. Similar to the resource-to-resource interconnect 624 of the sled 400 discussed above, the controller-to-controller interconnect 1242 may be embodied as any type of communication interconnect capable of facilitating controller-to-controller communications. In the illustrative embodiment, the controller-to-controller interconnect 1242 is embodied as a high-speed point-to-point interconnect (e.g., faster than the I/O subsystem 622). For example, the controller-to-controller interconnect 1242 may be embodied as a QuickPath Interconnect (QPI), an UltraPath Interconnect (UPI), or other high-speed point-to-point interconnect dedicated to processor-to-processor communications.

Referring now to FIG. 13, an illustrative embodiment of the storage sled 1200 is shown. In the illustrative embodiment, the data storage 1250 is embodied as, or otherwise includes, a storage cage 1252 configured to house one or more solid state drives (SSDs) 1254. To do so, the storage cage 1252 includes a number of mounting slots 1256, each of which is configured to receive a corresponding solid state drive 1254. Each of the mounting slots 1256 includes a number of drive guides 1258 that cooperate to define an access opening 1260 of the corresponding mounting slot 1256. The storage cage 1252 is secured to the chassis-less circuit board substrate 602 such that the access openings face away from (i.e., toward the front of) the chassis-less circuit board substrate 602. As such, solid state drives 1254 are accessible while the storage sled 1200 is mounted in a corresponding rack 204. For example, a solid state drive 1254 may be swapped out of a rack 240 (e.g., via a robot) while the storage sled 1200 remains mounted in the corresponding rack 240.

The storage cage 1252 illustratively includes sixteen mounting slots 1256 and is capable of mounting and storing sixteen solid state drives 1254. Of course, the storage cage 1252 may be configured to store additional or fewer solid state drives 1254 in other embodiments. Additionally, in the illustrative embodiment, the solid state drivers are mounted vertically in the storage cage 1252, but may be mounted in the storage cage 1252 in a different orientation in other embodiments. Each solid state drive 1254 may be embodied as any type of data storage device capable of storing long term data. To do so, the solid state drives 1254 may include volatile and non-volatile memory devices discussed above.

As shown in FIG. 13, the storage controllers 1220, the communication circuit 830, and the optical data connector 834 are illustratively mounted to the top side 650 of the chassis-less circuit board substrate 602. Again, as discussed above, any suitable attachment or mounting technology may be used to mount the electrical components of the storage sled 1200 to the chassis-less circuit board substrate 602 including, for example, sockets (e.g., a processor socket), holders, brackets, soldered connections, and/or other mounting or securing techniques.

As discussed above, the individual storage controllers 1220 and the communication circuit 830 are mounted to the top side 650 of the chassis-less circuit board substrate 602 such that no two heat-producing, electrical components shadow each other. For example, the storage controllers 1220 and the communication circuit 830 are mounted in corresponding locations on the top side 650 of the chassis-less circuit board substrate 602 such that no two of those electrical components are linearly in-line with each other along the direction of the airflow path 608.

The memory devices 720 of the storage sled 1200 are mounted to the bottom side 750 of the of the chassis-less circuit board substrate 602 as discussed above in regard to the sled 400. Although mounted to the bottom side 750, the memory devices 720 are communicatively coupled to the storage controllers 1220 located on the top side 650 via the I/O subsystem 622. Again, because the chassis-less circuit board substrate 602 is embodied as a double-sided circuit board, the memory devices 720 and the storage controllers 1220 may be communicatively coupled by one or more vias, connectors, or other mechanisms extending through the chassis-less circuit board substrate 602. Each of the storage controllers 1220 includes a heatsink 1270 secured thereto. As discussed above, due to the improved thermal cooling characteristics of the chassis-less circuit board substrate 602 of the storage sled 1200, none of the heatsinks 1270 include cooling fans attached thereto. That is, each of the heatsinks 1270 is embodied as a fan-less heatsink.

Referring now to FIG. 14, in some embodiments, the sled 400 may be embodied as a memory sled 1400. The storage sled 1400 is optimized, or otherwise configured, to provide other sleds 400 (e.g., compute sleds 800, accelerator sleds 1000, etc.) with access to a pool of memory (e.g., in two or more sets 1430, 1432 of memory devices 720) local to the memory sled 1200. For example, during operation, a compute sled 800 or an accelerator sled 1000 may remotely write to and/or read from one or more of the memory sets 1430, 1432 of the memory sled 1200 using a logical address space that maps to physical addresses in the memory sets 1430, 1432. The memory sled 1400 includes various components similar to components of the sled 400 and/or the compute sled 800, which have been identified in FIG. 14 using the same reference numbers. The description of such components provided above in regard to FIGS. 6, 7, and 8 apply to the corresponding components of the memory sled 1400 and is not repeated herein for clarity of the description of the memory sled 1400.

In the illustrative memory sled 1400, the physical resources 620 are embodied as memory controllers 1420. Although only two memory controllers 1420 are shown in FIG. 14, it should be appreciated that the memory sled 1400 may include additional memory controllers 1420 in other embodiments. The memory controllers 1420 may be embodied as any type of processor, controller, or control circuit capable of controlling the writing and reading of data into the memory sets 1430, 1432 based on requests received via the communication circuit 830. In the illustrative embodiment, each memory controller 1420 is connected to a corresponding memory set 1430, 1432 to write to and read from memory devices 720 within the corresponding memory set 1430, 1432 and enforce any permissions (e.g., read, write, etc.) associated with sled 400 that has sent a request to the memory sled 1400 to perform a memory access operation (e.g., read or write).

In some embodiments, the memory sled 1400 may also include a controller-to-controller interconnect 1442. Similar to the resource-to-resource interconnect 624 of the sled 400 discussed above, the controller-to-controller interconnect 1442 may be embodied as any type of communication interconnect capable of facilitating controller-to-controller communications. In the illustrative embodiment, the controller-to-controller interconnect 1442 is embodied as a high-speed point-to-point interconnect (e.g., faster than the I/O subsystem 622). For example, the controller-to-controller interconnect 1442 may be embodied as a QuickPath Interconnect (QPI), an UltraPath Interconnect (UPI), or other high-speed point-to-point interconnect dedicated to processor-to-processor communications. As such, in some embodiments, a memory controller 1420 may access, through the controller-to-controller interconnect 1442, memory that is within the memory set 1432 associated with another memory controller 1420. In some embodiments, a scalable memory controller is made of multiple smaller memory controllers, referred to herein as “chiplets”, on a memory sled (e.g., the memory sled 1400). The chiplets may be interconnected (e.g., using EMIB (Embedded Multi-Die Interconnect Bridge)). The combined chiplet memory controller may scale up to a relatively large number of memory controllers and I/O ports, (e.g., up to 16 memory channels). In some embodiments, the memory controllers 1420 may implement a memory interleave (e.g., one memory address is mapped to the memory set 1430, the next memory address is mapped to the memory set 1432, and the third address is mapped to the memory set 1430, etc.). The interleaving may be managed within the memory controllers 1420, or from CPU sockets (e.g., of the compute sled 800) across network links to the memory sets 1430, 1432, and may improve the latency associated with performing memory access operations as compared to accessing contiguous memory addresses from the same memory device.

Further, in some embodiments, the memory sled 1400 may be connected to one or more other sleds 400 (e.g., in the same rack 240 or an adjacent rack 240) through a waveguide, using the waveguide connector 1480. In the illustrative embodiment, the waveguides are 64 millimeter waveguides that provide 16 Rx (i.e., receive) lanes and 16 Tx (i.e., transmit) lanes. Each lane, in the illustrative embodiment, is either 16 GHz or 32 GHz. In other embodiments, the frequencies may be different. Using a waveguide may provide high throughput access to the memory pool (e.g., the memory sets 1430, 1432) to another sled (e.g., a sled 400 in the same rack 240 or an adjacent rack 240 as the memory sled 1400) without adding to the load on the optical data connector 834.

Referring now to FIG. 15, a system for executing one or more workloads (e.g., applications) may be implemented in accordance with the data center 100. In the illustrative embodiment, the system 1510 includes an orchestrator server 1520, which may be embodied as a managed node comprising a compute device (e.g., a processor 820 on a compute sled 800) executing management software (e.g., a cloud operating environment, such as OpenStack) that is communicatively coupled to multiple sleds 400 including a large number of compute sleds 1530 (e.g., each similar to the compute sled 800), memory sleds 1540 (e.g., each similar to the memory sled 1400), accelerator sleds 1550 (e.g., each similar to the memory sled 1000), and storage sleds 1560 (e.g., each similar to the storage sled 1200). One or more of the sleds 1530, 1540, 1550, 1560 may be grouped into a managed node 1570, such as by the orchestrator server 1520, to collectively perform a workload (e.g., an application 1532 executed in a virtual machine or in a container). The managed node 1570 may be embodied as an assembly of physical resources 620, such as processors 820, memory resources 720, accelerator circuits 1020, or data storage 1250, from the same or different sleds 400. Further, the managed node may be established, defined, or “spun up” by the orchestrator server 1520 at the time a workload is to be assigned to the managed node or at any other time, and may exist regardless of whether any workloads are presently assigned to the managed node. In the illustrative embodiment, the orchestrator server 1520 may selectively allocate and/or deallocate physical resources 620 from the sleds 400 and/or add or remove one or more sleds 400 from the managed node 1570 as a function of quality of service (QoS) targets (e.g., performance targets associated with a throughput, latency, instructions per second, etc.) associated with a service level agreement for the workload (e.g., the application 1532). In doing so, the orchestrator server 1520 may receive telemetry data indicative of performance conditions (e.g., throughput, latency, instructions per second, etc.) in each sled 400 of the managed node 1570 and compare the telemetry data to the quality of service targets to determine whether the quality of service targets are being satisfied. The orchestrator server 1520 may additionally determine whether one or more physical resources may be deallocated from the managed node 1570 while still satisfying the QoS targets, thereby freeing up those physical resources for use in another managed node (e.g., to execute a different workload). Alternatively, if the QoS targets are not presently satisfied, the orchestrator server 1520 may determine to dynamically allocate additional physical resources to assist in the execution of the workload (e.g., the application 1532) while the workload is executing. Similarly, the orchestrator server 1520 may determine to dynamically deallocate physical resources from a managed node if the orchestrator server 1520 determines that deallocating the physical resource would result in QoS targets still being met.

Additionally, in some embodiments, the orchestrator server 1520 may identify trends in the resource utilization of the workload (e.g., the application 1532), such as by identifying phases of execution (e.g., time periods in which different operations, each having different resource utilizations characteristics, are performed) of the workload (e.g., the application 1532) and pre-emptively identifying available resources in the data center 100 and allocating them to the managed node 1570 (e.g., within a predefined time period of the associated phase beginning). In some embodiments, the orchestrator server 1520 may model performance based on various latencies and a distribution scheme to place workloads among compute sleds and other resources (e.g., accelerator sleds, memory sleds, storage sleds) in the data center 100. For example, the orchestrator server 1520 may utilize a model that accounts for the performance of resources on the sleds 400 (e.g., FPGA performance, memory access latency, etc.) and the performance (e.g., congestion, latency, bandwidth) of the path through the network to the resource (e.g., FPGA). As such, the orchestrator server 1520 may determine which resource(s) should be used with which workloads based on the total latency associated with each potential resource available in the data center 100 (e.g., the latency associated with the performance of the resource itself in addition to the latency associated with the path through the network between the compute sled executing the workload and the sled 400 on which the resource is located).

In some embodiments, the orchestrator server 1520 may generate a map of heat generation in the data center 100 using telemetry data (e.g., temperatures, fan speeds, etc.) reported from the sleds 400 and allocate resources to managed nodes as a function of the map of heat generation and predicted heat generation associated with different workloads, to maintain a target temperature and heat distribution in the data center 100. Additionally or alternatively, in some embodiments, the orchestrator server 1520 may organize received telemetry data into a hierarchical model that is indicative of a relationship between the managed nodes (e.g., a spatial relationship such as the physical locations of the resources of the managed nodes within the data center 100 and/or a functional relationship, such as groupings of the managed nodes by the customers the managed nodes provide services for, the types of functions typically performed by the managed nodes, managed nodes that typically share or exchange workloads among each other, etc.). Based on differences in the physical locations and resources in the managed nodes, a given workload may exhibit different resource utilizations (e.g., cause a different internal temperature, use a different percentage of processor or memory capacity) across the resources of different managed nodes. The orchestrator server 1520 may determine the differences based on the telemetry data stored in the hierarchical model and factor the differences into a prediction of future resource utilization of a workload if the workload is reassigned from one managed node to another managed node, to accurately balance resource utilization in the data center 100.

To reduce the computational load on the orchestrator server 1520 and the data transfer load on the network, in some embodiments, the orchestrator server 1520 may send self-test information to the sleds 400 to enable each sled 400 to locally (e.g., on the sled 400) determine whether telemetry data generated by the sled 400 satisfies one or more conditions (e.g., an available capacity that satisfies a predefined threshold, a temperature that satisfies a predefined threshold, etc.). Each sled 400 may then report back a simplified result (e.g., yes or no) to the orchestrator server 1520, which the orchestrator server 1520 may utilize in determining the allocation of resources to managed nodes.

Referring now to FIG. 16, a system 1600 for providing communication abstraction for accelerator device kernels includes multiple accelerator sleds 1610, 1612, and a compute sled 1614 in communication with each other and with an orchestrator server 1616, which may also be referred to as a telemetry service device. The compute sled 1614 may be similar to the compute sled 800, and the processor 1680 and network interface controller (NIC) 1684 may be similar to the processor 820 and NIC 832, respectively. Each accelerator sled 1610, 1612 is similar to the accelerator sled 1000 of FIG. 10. While two accelerator sleds 1610, 1612 are shown for clarity, it should be understood that the system 1600 may have a different number of accelerator sleds (e.g., tens, hundreds, or thousands) and may include other types of sleds (memory, storage, etc.). In the illustrative embodiment, the accelerator sled 1610 includes two accelerator devices 1620, 1622, similar to the accelerator circuits 1020 of the accelerator sled 1000 of FIG. 10. In the illustrative embodiment, each accelerator device 1620, 1622 is an FPGA. The gates of the FPGA 1620 are partitioned into two slots 1630, 1632 (e.g., each a subset of the gates present in the FPGA 1620). Each slot 1630, 1632 implements a corresponding kernel 1660, 1662, each of which may be embodied as a set of gates configured to perform a set of functions (e.g., operations offloaded from a compute sled, such as the compute sled 1614, to increase the speed at which a workload (e.g., the application 1682 executed by a processor 1680) is performed on behalf of a customer, also referred to herein as a tenant). Additionally, each slot 1630, 1632, in the illustrative embodiment, includes a communication abstraction logic unit 1640, 1642 which may be embodied as any device or circuitry (e.g., a subset of the gates of the corresponding slot 1630, 1632, a processor, a controller, etc.) configured to enable a corresponding kernel to easily (e.g., through an application programming interface exposed to a high level software language (also referred to as a high level programming language), such as OpenCL, Verilog, C/C++, etc.) communicate with other kernels to cooperatively execute a workload (e.g., sequentially, in parallel, etc.). In doing so, the communication abstraction logic unit 1640, 1642 may enable the corresponding kernel to cooperatively execute a workload with another accelerator device on the same sled, a scenario referred to herein as “scale-up,” and/or may enable the kernel to cooperatively execute the workload with one or more kernels on other sleds, a scenario referred to herein as “scale-out.”

To support scale-up and/or scale-out, the communication abstraction logic unit 1640, 1642 manages (e.g., abstracts) differences in the kernels, the underlying architectures of the accelerator devices, and the physical communication paths through which the kernels are connected to each other (e.g., through a unified protocol that utilizes an inter-chip communication protocol, such as Peripheral Component Interconnect Express, Intel SerialLite, etc. for communications between kernels present on the same sled (e.g., in different slots of the same accelerator device or between accelerator devices on the same sled) and a network-based protocol, such as remote direct memory access (RDMA) over Ethernet, for communication between kernels implemented on different sleds in the system 1600). Relatedly, the communication abstraction logic unit 1640, 1642 is configured to identify the other accelerator devices and accelerator device kernels available in the system 1600, identify the physical communication path(s) between the corresponding kernel 1660, 1662 and the identified accelerator devices and kernels in the system 1600, and selectively establish a logical communication path (e.g., using the unified communication protocol) with one or more of the accelerator devices and their associated kernel(s) (e.g., at the request of the corresponding kernel 1660, 1662). The physical communication paths, as indicated above, are embodied as the underlying buses and networking connections (e.g., PCIe, Ethernet, optical fiber, waveguides, etc.) within an accelerator device, between accelerator devices on the same sled, or between sleds and a logical communication connection is an abstraction of the one or more of the physical communication paths, exposed by the communication abstraction logic unit 1640, 1642 to the corresponding kernel 1660, 1662.

The accelerator device 1622 includes slots 1634, 1636, similar to the slots 1630, 1632 described above. Further, each slot 1634, 1636 includes a corresponding kernel 1664, 1666 and communication abstraction logic unit 1644, 1646, similar to the communication abstraction logic units 1640, 1642 described above. Additionally, the accelerator sled 1612 includes accelerator devices 1624 and 1626. The accelerator device 1624, in the illustrative embodiment, is a graphics processing unit (GPU), which may be embodied as any device or circuitry (e.g., a programmable logic chip, a processor, etc.) configured to perform graphics-related computations (e.g., matrix multiplication, vector operations, etc.), and the accelerator device 1626, in the illustrative embodiment, is a vision processing unit (VPU), which may be embodied as any device or circuitry (e.g., a programmable logic chip, a processor, etc.) configured to perform operations related to machine vision, machine learning, and artificial intelligence. Each accelerator device 1624, 1626, in the illustrative embodiment, includes a corresponding kernel 1668, 1670 and communication abstraction logic unit 1648, 1650, similar to the communication abstraction logic units 1640, 1642 described above (e.g., each communication abstraction logic unit 1648, 1650 exposes functions through an application programming interface (API) that is accessible to a high level software programming language in which the corresponding kernel 1668, 1670 is implemented and manages, with a unified communication protocol, differences between physical communication paths in the system 1600). While, in the illustrative embodiment, each slot of the accelerator devices 1620, 1622 (FPGAs) includes a corresponding communication abstraction logic unit, in other embodiments, there may be one communication abstraction logic unit per accelerator device, or one communication abstraction logic unit per sled.

In use, various components such as the communication abstraction logic unit 1640 and the NIC 1684 may perform compression operations in order to facilitate communication across the inter-kernel link when bandwidth for communication between the accelerator devices may be limited. For example, in some embodiments, a component of the compute sled 1614 such as the processor 1680 and/or the NIC 1684 may compress one or more parameters for a workload that is to be performed by one or more of the kernels 1660, 1662, 1664, 1666, 1668, and 1670 prior to sending the parameters for the workload to the corresponding accelerator device. In determining whether and how to compress the one or more parameters, the processor 1680 and/or the NIC 1684 may receive telemetry data about the system of accelerator devices (i.e., the accelerator sled 1610, the accelerator sled 1612, the physical and logical communication paths, etc.). The telemetry data may indicate, e.g., bandwidth availability, latency of communication, resource usage of one or more of the accelerator devices, etc. The processor 1680 and/or the NIC 1684 may, e.g., determine that compression should be used based on telemetry data indicating that there is low bandwidth availability or may determine that compression should not be used based on telemetry data indicating that there is high bandwidth availability. It should be appreciated that, in some embodiments, the analysis of the telemetry data and the compression may be done without any instruction to do so from the application 1682 that is initiating or controlling the workload or even without any knowledge by the application 1682 that the analysis and compression is being done. In some embodiments, the processor 1680 and/or the NIC 1684 may send the parameters to the accelerator device using remote direct memory access (RDMA). For example, the processor 1680 may transmit an RDMA command through the NIC 1684. The NIC 1684 may compress the data to be sent through the RDMA command before sending it to the desired communication abstraction logic unit 1640, which can then decompress the data and perform the RDMA command.

Additionally or alternatively, in some embodiments, the communication abstraction logic unit 1640 may be configured to compress and decompress communication from and to the kernel 1660. For example, after receiving one or more workload parameters for a workload to be performed on the kernel 1660, the communication abstraction logic unit 1640 may be configured to copy the one or more workload parameters to one or more additional accelerator devices, and the communication abstraction logic unit 1640 may compress the data prior to sending the parameters to the other accelerator devices. In some embodiments, the communication abstraction logic 1640 may be configured to compress communication between the kernel 1660 and other kernels that are working on the same or a related workload. For example, the kernel 1660 sends data to another kernel by transmitting an RDMA command through the communication abstraction logic 1640. The communication abstraction logic 1640 may compress the data to be sent through the RDMA command before sending it to communication abstract logic unit corresponding to the destination kernel.

In determining whether and how to compress the one or more parameters, the communication logic unit 1640 may receive telemetry data about the system of accelerator devices (i.e., the accelerator sled 1610, the accelerator sled 1612, the physical and logical communication paths, the kernels 1660, 1662, 1664, 1666, 1668, and 1670, etc.). The telemetry data may indicate, e.g., bandwidth availability, latency of communication, resource usage of one or more of the accelerator devices, etc. The communication abstraction logic 1640 may, e.g., determine that compression should be used based on telemetry data indicating that there is low bandwidth availability or may determine that compression should not be used based on telemetry data indicating that there is high bandwidth availability. The communication abstraction logic unit 1640 may perform analysis of the telemetry as well as compression and decompression of data without any instruction from the kernel 1660 to do so or even without any knowledge by the kernel 1660 that the analysis and compression is being done.

Referring now to FIG. 17, the accelerator device 1620, in operation, may perform a method for providing communication abstraction (e.g., abstraction of inter-kernel communication) for an accelerator device kernel (e.g., the kernel 1660) to support scale-up (e.g., cooperatively executing a workload with other kernel(s) on the same sled) and/or scale-out (e.g., cooperatively executing a workload with kernel(s) on other sled(s)). While the method 1700 is described as being performed by the accelerator device 1620, it should be understood that any other accelerator device 1622, 1624, 1626 may also perform the method 1700. The method 1700 begins with block 1702, in which the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) determines whether to enable inter-kernel communication abstraction (e.g., to support scale-up and/or scale-out). In the illustrative embodiment, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may determine to enable inter-kernel communication abstraction in response to determining that the accelerator device 1620 is equipped with a communication abstraction logic unit (e.g., the communication abstraction logic unit 1640). In other embodiments, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may determine to enable inter-kernel communication abstraction based on other factors. Regardless, in response to a determination to enable inter-kernel communication abstraction, the method 1700 advances to block 1704 in which the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) obtains availability data indicative of the availability of accelerator device kernels (e.g., of the accelerator devices and the kernels implemented on the accelerator devices) in the system 1600. In doing so, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) obtains data indicative of types of accelerator devices and kernels available in the system 1600, as indicated in block 1706. For example, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may receive data indicating that the accelerator sled 1610 includes two accelerator devices 1620, 1622, which are both FPGAs, and that each FPGA 1620, 1622 has two slots 1630, 1632, 1634, 1636 with a corresponding kernel.

Further, the availability data, in the illustrative embodiment, indicates the set of functions that the kernel in each slot of each FPGA is capable of performing. Further, the availability data, in the illustrative embodiment indicates that the accelerator sled 1612 includes the GPU 1624 and that the corresponding kernel 1668 supports a corresponding set of functions (e.g., matrix multiplication operations), and that the accelerator sled 1612 includes the VPU 1626 and that the corresponding kernel 1670 supports a corresponding set of functions (e.g., object recognition operations, neural network training operations, etc.). Additionally, as indicated in block 1708, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may obtain data indicative of a present capacity of each accelerator device 1620, 1622, 1624, 1626 in the system 1600. For example, and as indicated in block 1710, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may obtain data indicative of a present load on each accelerator device (e.g., a percentage of total computational throughput being used, a number of operations per second presently being performed, etc.). As indicated in block 1712, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may obtain data indicative of deactivated accelerator devices (e.g., accelerator devices with no capacity) in the system 1600.

In the illustrative embodiment, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) obtains availability data indicative of a location of each accelerator device kernel (e.g., the kernels 1662, 1664, 1666, 1668, 1670) in the system 1600, as indicated in block 1714. In doing so, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may obtain data indicative of physical communication paths to each accelerator device kernel, as indicated in block 1716. For example, and as indicated in block 1718, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) obtains data indicative of physical communication paths between sleds 1610, 1612 (e.g., an Ethernet connection, an optical fiber connection, a waveguide connection, an inter-chip connection, etc., including any switches or other intermediary devices). The accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may obtain data indicative of physical communication paths between accelerator devices (e.g., between the accelerator device 1620, 1622) on the same sled (e.g., on the sled 1610), such as a peripheral component interconnect express (PCIe) bus, a serial interconnect, or other local bus, as indicated in block 1720. As indicated in block 1722, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may obtain data indicative of physical communication paths between portions of the same accelerator device (e.g., between slots 1630, 1632 of the same FPGA 1620). The accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may additionally obtain data indicative of congestion on each physical communication path (e.g., a latency, an amount of packets sent per second, an amount of data sent per second, a percentage of the total bandwidth of the physical communication path being used, etc.), as indicated in block 1724. As indicated in block 1726, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) receives availability data from other accelerator devices (e.g., from the communication abstraction logic units 1642, 1644, 1646, 1648, 1650). Additionally or alternatively, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may receive the availability data from a telemetry service device (e.g., the orchestrator server 1616), as indicated in block 1728. Subsequently, the method 1700 advances to block 1730 of FIG. 18, in which the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) sends availability data to other devices in the system 1600.

Referring now to FIG. 18, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640), in sending the availability data, may send availability data pertaining to the present accelerator device 1620 to one or more devices, as indicated in block 1732. As indicated in block 1734, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may send availability data pertaining to another accelerator device to one or more devices. For example, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may send the availability data to other accelerator devices in the system 1600, as indicated in block 1736. As indicated in block 1738, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may send the availability data to a telemetry service device (e.g., the orchestrator server 1616).

Still referring to FIG. 18, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640), may receive a request to establish a logical communication path (e.g., a representation of a communication path that maps to one or more physical communication paths) with another accelerator device kernel (e.g., one or more of kernels 1662, 1666, 1668, 1670), as indicated in block 1740. In doing so, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may receive a request from a kernel executed on the present accelerator sled (e.g., the kernel 1660 on the FPGA 1620 of the sled 1610), as indicated in block 1742. For example, and as indicated in block 1744, the communication abstraction logic unit 1640 may receive the request from the corresponding kernel (e.g., the kernel 1660) through an application programming interface (API) that is exposed to (e.g., accessible through one or more function calls in) a high level software language (e.g., OpenCL, Verilog, C/C++, etc.) in which the kernel (e.g., the kernel 1660) is implemented (e.g., defined). As indicated in block 1746, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640), may receive a request that identifies a type of kernel to communicate with (e.g., data including a name or other identifier of a kernel in the system 1600, data indicative of one or more functions supported by the kernel to be communicated with, etc.). The request may also include data indicative of the type of accelerator device hosting the kernel to be communicated with (e.g., an FPGA, a GPU, a VPU, etc.), as indicated in block 1748. As indicated in block 1750, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may receive a request that identifies one or more target quality of service parameters for the requested communication with the other kernel(s). For example, and as indicated in block 1752, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may receive a request that specifies a target latency (e.g., a maximum amount of time that may elapse for a packet to be delivered to the other kernel).

As indicated in block 1754, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may receive a request that specifies a target bandwidth. Additionally or alternatively, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may receive a request that references a service level agreement (SLA) that defines one or more quality of service parameters for the logical communication path with the other kernel(s), as indicated in block 1756. As indicated in block 1758, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may alternatively determine the target quality of service parameter(s) from an identity of a tenant (e.g., customer) associated with the requesting kernel and an SLA associated with the tenant. Subsequently, the method 1700 advances to block 1760 of FIG. 19, in which the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) determines the subsequent course of action based on whether a request to establish a logical communication path (e.g., in block 1740) has been received.

Referring now to FIG. 19, if a request to establish a logical communication path was not received, the method 1700 loops back to block 1704 of FIG. 17, in which the accelerator device 1620 continues to obtain availability data (e.g., from other accelerator devices and/or from the orchestrator server 1616). Otherwise, if a request to establish a logical communication path was received, the method 1700 advances to block 1762, in which the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) establishes a logical communication path with the other accelerator device kernel(s) using the availability data (e.g., the availability data obtained in block 1704 of FIG. 17). In doing so, and as indicated in block 1764, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) selects target accelerator device(s) (e.g., accelerator devices to communicate with) and corresponding kernels, based on the requested types (e.g., the accelerator device type and kernel type from blocks 1746, 1748). In block 1766, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640), determines path(s) to the target accelerator device kernel(s). In doing so, and as indicated in block 1768, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) determines a shortest path to the accelerator device kernel(s) as a function of the availability data. As indicated in block 1770, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may determine the path having the lowest latency. Additionally or alternatively, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may determine the path having the lowest number of intermediary devices (e.g., switches, other accelerator devices through which the target accelerator device is connected to the accelerator device 1620, etc.), as indicated in block 1772. Additionally or alternatively, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may determine the path having the least amount of congestion, as indicated in block 1774. In block 1776, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may allocate multiple physical communication paths to a single logical communication path based on the target quality of service parameters (e.g., combining multiple physical communication paths to obtain a target bandwidth).

As indicated in block 1778, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may allocate less than the total amount of communication capacity of a given physical communication path to the logical communication path, based on the target quality of service parameters (e.g., the target bandwidth is less than the total available bandwidth of a given physical communication path). As indicated in block 1780, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may reroute an existing logical communication path (e.g., to utilize different physical communication path(s), to utilize more of the available capacity of a physical communication path, etc.), to maintain a target quality of service (e.g., a target bandwidth, a target latency, etc.). In the illustrative embodiment, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) sends data indicative of the established logical communication path (e.g., the selected physical communication path(s) and the capacities of the physical communication path(s) to be allocated to a given logical communication path) to the target kernels (e.g., to the communication abstraction logic unit 1642, 1646, 1648, 1650 associated with the target kernel), as indicated in block 1782. Subsequently, the method 1700 advances to block 1784 of FIG. 20, in which the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) communicates data between accelerator device kernels using the established logical communication path(s).

Referring now to FIG. 20, in communicating the data, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) communicates data through the corresponding physical communication path(s), as indicated in block 1786. In doing so, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may add one or more headers to packets associated with the established logical communication path(s) (e.g., routing data to enable the packet(s) to travel through the corresponding physical communication path(s) to the target kernel(s)), as indicated in block 1788. Additionally, in the illustrative embodiment, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may remove headers from incoming packets, as indicated in block 1790. As indicated in block 1792, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may utilize multiple physical communication paths for a single logical communication path. For example, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may utilize multiple physical I/O ports, as indicated in block 1794 and/or may utilize multiple physical network interface controllers (NICs), as indicated in block 1796.

As indicated in block 1798, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may utilize less than all of the available capacity of a physical communication path to communicate data with the target kernel(s). Further, and as indicated in block 1800, in communicating data between accelerator device kernels, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) utilizes a unified communication protocol that manages any differences between the physical communication paths connecting the accelerator device kernels, any differences in the architectures of the underlying accelerator devices on which the kernels are implemented, and any differences in the implementations of the kernels (e.g., differences in the high level software languages used to define the kernels). In the illustrative embodiment, and as indicated in block 1802, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may use an inter-chip communication protocol (e.g., an inter-chip communication protocol included within the unified communication protocol) to communicate data between kernels that are on the same accelerator device or sled (e.g., for communication of data between the kernel 1660 and any of the kernels 1662, 1664, 1666). The inter-chip communication protocol may be, include, or utilize, Peripheral Component Interconnect Express (PCIe), Intel SerialLite, or any other inter-chip communication protocol. As indicated in block 1804, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may utilize a network-based protocol (e.g., a network-based protocol included within the unified communication protocol) to communicate data between kernels (e.g., the kernel 1660 and kernels 1668 and/or 1670) on different sleds (e.g., between the sleds 1610, 1612). For example, and as indicated in block 1806, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may utilize remote direct memory access (RDMA) over Ethernet to communicate data (e.g., utilizing memory references) between kernels (e.g., the kernel 1660 and kernels 1668 and/or 1670) on different sleds (e.g., between the sleds 1610, 1612). As such, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) selects the appropriate communication protocol within the unified communication protocol to enable communication of data to take place over the corresponding physical communication path.

As indicated in block 1808, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may communicate data between accelerator devices having different architectures. For example, the accelerator device 1620, in the illustrative embodiment, is an FPGA and may communicate (e.g., using the communication abstraction logic unit 1640) with a GPU (e.g., through the communication abstraction logic unit 1648 of the GPU 1624), a VPU (e.g., through the communication abstraction logic unit 1650 of the VPU 1626), and/or other accelerator devices having other architectures. Similarly, and as indicated in block 1810, the accelerator device 1620 (e.g., the communication abstraction logic unit 1640) may communicate data between accelerator device kernels that are implemented with different high level software languages (e.g., OpenCL, Verilog, C/C++, etc.). The differences may be managed by using a standardized set of data formats (e.g., to define data structures) for communication between the communication abstraction logic units 1640, 1642, 1644, 1646, 1648, 1650 and each communication abstraction logic unit 1640, 1642, 1644, 1646, 1648, 1650 may further implement a encoding and decoding layer to translate to and from a native data format used by the corresponding kernel and accelerator device architecture. As such, the differences in physical communication paths, accelerator device architectures, and/or kernel implementations (e.g., high level software languages used to implement the kernels) in the system 1600 are abstracted away from the kernel (e.g., the kernel 1660) that requested to communicate with one or more other kernels (e.g., to scale-up and/or scale-out the execution of the workload), thereby opening up the possibility of communicating with a wider range of accelerator devices and kernels present in the system 1600 and allowing the developer (e.g., programmer) of the kernel 1660 to focus on implementation details for accelerating the execution of a particular workload rather than the details of enabling communication with the kernels (e.g., the kernels 1662, 1664, 1666, 1668, 1670) present in the system 1600.

In the illustrative embodiment, the method 1700 subsequently loops back to block 1704 of FIG. 17, to continue to obtain availability data. While shown as being performed in a particular sequence, it should be understood that the operations described with reference to the method 1700 may be performed in a different order and/or concurrently (e.g., the accelerator device 1620 may continually obtain availability data while the accelerator device 1620 is concurrently sending and receiving data between kernels and rerouting logical communication paths through the available physical communication paths).

Referring now to FIG. 21, the compute device 1614, in operation, may perform a method 2100 for compressing and decompressing communication from and to the compute device 1614. While the method 2100 is described as being performed by the compute device 1614, it should be understood that some or all of the method 2100 may be performed by particular components of the compute device 1614, such as the processor 1680 and/or the NIC 1684. The method 2100 begins with block 2102, in which the compute device 1614 determines a workload to be performed on one or more accelerator devices. In block 2104, the compute device 1614 determines one or more parameters of the workload that are to be loaded into memory of the one or more accelerator devices.

In block 2106, the compute device 1614 receives telemetry data indicative of a performance parameter of a system of accelerator devices including the one or more accelerator (e.g., the accelerator sled 1610, the accelerator sled 1612, the physical and logical communication paths, etc.). The telemetry data may indicate, e.g., bandwidth availability, latency of communication, resource usage of one or more of the accelerator devices, etc. The compute device 1614 may receive the telemetry data from the orchestrator server 1616, from an accelerator device, or from any other device of the system 1600. The compute device 1614 may receive the telemetry data continuously, continually, periodically, and/or upon request.

In block 2108, the compute device 1614 determines whether compression should be used to compress the one or more parameters of the workload prior to sending the one or more parameters to the one or more accelerator devices based on the telemetry data. To do so, the compute device 1614 may analyze the telemetry data. The compute device 1614 may use any suitable analysis of the telemetry data in order to determine whether compression should be used. For example, the compute device 1614 may determine that compression should be used based on telemetry data indicating that there is low bandwidth availability or may determine that compression should not be used based on telemetry data indicating that there is high bandwidth availability. In some embodiments, the compute device 1614 may select a compression algorithm to use based on the telemetry data in block 2110. For example, the compute device 1614 may select a high-compression-ratio compression algorithm that is more computationally expensive if the bandwidth availability is below a threshold but may select a compression algorithm with a lower compression ratio that is less computationally expensive if the bandwidth availability is above the threshold in block 2112. If a compression algorithm is to be used, the compute device 1614 compresses the one or more parameters of the workload in block 2114.

Additionally or alternatively, in some embodiments, the compute device 1614 may determine whether to use a compression algorithm and/or select a particular compression algorithm based on knowledge about the data to be sent. If the compute device 1614 knows that the data is of a particular type, the compute device 1614 may select a compression algorithm that has good performance such as fast compression and/or high compression ratio for that type of data. For example, the compute device 1614 may know that some or all of the parameters being sent are matrix parameters that follow a particular format used in an artificial intelligence algorithm, the compute device 1614 may select a compression algorithm that is suitable to that type of compression.

In block 2116, the compute device 1614 sends the one or more workload parameters to the one or more accelerator devices. If the workload parameters were compressed, the compute device 1614 sends the compressed workload parameters in block 2118. In some embodiments, the compute device 1614 may send the one or more workload parameters as one or more RDMA commands with the data in the RDMA commands compressed. Upon receipt, the data in the RDMA command may be decompressed and the RDMA command can be performed.

In block 2120, in some embodiments, the compute device 1614 receives the workload result from the accelerator device after the workload is completed. In the illustrative embodiment, the workload result is compressed, and the compute device 1614 may decompress it. In other embodiments, the workload result may be sent without being compressed.

Referring now to FIG. 22, the accelerator device 1620, in operation, may perform a method for compressing and decompressing communication from and to the accelerator device 1620. While the method 2200 is described as being performed by the accelerator device 1620, it should be understood that any other accelerator device 1622, 1624, 1626 may also perform the method 2200. The method 2200 begins with block 2202, in which the accelerator device 1620 sends telemetry data. The accelerator device 1620 may send the telemetry data to, e.g., the orchestrator server 1616 or to the compute device 1614. In some embodiments, the accelerator device 1620 may send the telemetry data to the compute device 1614 by sending the telemetry data to the orchestrator server 1616 that then relays the telemetry data (with or without further processing) to the compute device 1614.

In block 2204, the accelerator device 1620 receives one or more workload parameters to be loaded into memory of the accelerator device 1620 for use by a kernel 1660. As discussed above in regard to the method 2100, the one or more workload parameters may be compressed based on the telemetry data sent in block 2202. In such embodiments, the accelerator device 1620 or a component of the accelerator device 1620 such as the communication abstraction logic unit 1640 may decompress the workload parameters before providing the workload parameters to the kernel 1660. It should be appreciated that, in some embodiments, the communication abstraction logic unit 1640 may decompress the data without involving the kernel 1660. In block 2206, the accelerator device 1620 provides the workload parameters to the kernel 1660.

In block 2208, the accelerator device 1620 sends and receives telemetry data to and from other accelerator devices. In some embodiments, the accelerator device 1620 may send the telemetry data to the other accelerator devices by sending the telemetry data to the orchestrator server 1616 that then relays the telemetry data (with or without further processing such as aggregation or analysis) to the other accelerator devices. Similarly, the accelerator device 1620 may receive telemetry data from the other accelerator devices by receiving the telemetry data from the orchestrator server 1616 that relayed the telemetry data it received from the other accelerator devices.

In block 2210, the accelerator device 1620 determines whether compression should be used to compress data to be sent to other accelerator devices. To do so, the accelerator device 1620 may analyze the telemetry data. The accelerator device 1620 may use any suitable analysis of the telemetry data in order to determine whether compression should be used. For example, the accelerator device 1620 may determine that compression should be used based on telemetry data indicating that there is low bandwidth availability or may determine that compression should not be used based on telemetry data indicating that there is high bandwidth availability. In some embodiments, the accelerator device 1620 may select a compression algorithm to use based on the telemetry data in block 2212. For example, the accelerator device 1620 may select a high-compression-ratio compression algorithm that is more computationally expensive if the bandwidth availability is below a threshold but may select a compression algorithm with a lower compression ratio that is less computationally expensive if the bandwidth availability is above the threshold in block 2214. If a compression algorithm is to be used, the accelerator device 1620 compresses the data to be sent in block 2216. It should be appreciated that the data to be sent could be either data that is sent as part of performing a workload by a kernel 1660 or may be data that is sent when not performing a workload by a kernel, such as the one or more parameters that may be loaded into multiple accelerator devices before operating a distributed workload.

In block 2218, the accelerator device 1620 may send the data to other accelerator devices. In block 2220, when compression is used, the accelerator device 1620 may send the compressed data to other accelerator devices. In some embodiments, the accelerator device 1620 may send the data as one or more RDMA commands with the data in the RDMA commands compressed. Upon receipt, the data in the RDMA command may be decompressed and the RDMA command can be performed.

In some embodiments, when the accelerator device 1620 is used to distribute one or more workload parameters to other accelerator devices with compression, the accelerator device 1620 may send the one or more compressed workload parameters to other accelerator devices 1620 in block 2222. It should be appreciated that, in some embodiments, a component of the accelerator device 1620 such as the communication abstraction logic unit 164 may receive data from the kernel 1660 to be sent to a kernel on another accelerator device, and that component may determine whether or not to compress the data and perform the compression without instruction from the kernel 1660 or even without the kernel 1660 having any knowledge of the compression. It should be appreciated that such embodiments may improve system performance while simplifying the task of programmers of the kernel 1660 by not requiring any explicit programming of compression in the kernel 1660.

In block 2224, the accelerator device 1620 may determine whether or not compression should be used to compress a workload result to be sent to the compute device 1614. The accelerator device 1620 may determine whether or not compression should be used based on the telemetry data in block 2226. In some embodiments, the accelerator device 1620 may select a compression algorithm to use based on the telemetry data in block 2228. For example, the accelerator device 1620 may select a high-compression-ratio compression algorithm that is more computationally expensive if the bandwidth availability is below a threshold but may select a compression algorithm with a lower compression ratio that is less computationally expensive if the bandwidth availability is above the threshold. In block 2230, the accelerator devices 1620 may compress the workload to be sent to the compute device 1614.

Additionally or alternatively, in some embodiments, the accelerator device 1620 may determine whether to use a compression algorithm and/or select a particular compression algorithm based on knowledge about the data to be sent. If the accelerator device 1620 knows that the data is of a particular type, the accelerator device 1620 may select a compression algorithm that has good performance such as fast compression and/or high compression ratio for that type of data. For example, the accelerator device 1620 may know that some or all of the parameters being sent are matrix parameters that follow a particular format used in an artificial intelligence algorithm, the accelerator device 1620 may select a compression algorithm that is suitable to that type of compression. In one embodiment, the kernel 1660 may provide to the communication abstraction logic unit 1640 one or more hints indicating a type of some or all of the data to be sent. The communication abstraction logic unit 1640 may then use that hint to determine whether compression should be done and/or to select a compression algorithm to be used.

In block 2232, the accelerator device 1620 may send the workload result to the compute device 1614. In the illustrative embodiment, the accelerator device 1620 may send the compressed workload result in block 2234. In some embodiments, the accelerator device 1620 may send the workload result as one or more RDMA commands with the data in the RDMA commands compressed. Upon receipt, the data in the RDMA command may be decompressed and the RDMA command can be performed.

Examples

Illustrative examples of the technologies disclosed herein are provided below. An embodiment of the technologies may include any one or more, and any combination of, the examples described below.

Example 1 includes an accelerator device comprising one or more slots; and circuitry to receive, from a compute device, one or more parameters of a workload, the workload to be performed a slot of the one or more slots of the accelerator device and one or more slots of one or more remote accelerator devices; provide the one or more parameters of the workload to a kernel of the one or more slots of the accelerator device; receive, from the kernel, a workload result based on the one or more parameters of the workload; automatically compress the workload result without instruction from the kernel; and send the compressed workload result to the compute device.

Example 2 includes the subject matter of Example 1, and wherein the circuitry is further to receive, from the kernel, data to be sent to a remote kernel on the one or more remote accelerator devices; compress the data received from the kernel without instruction from the kernel; and send the compressed data to the one or more remote accelerator devices.

Example 3 includes the subject matter of any of Examples 1 and 2, and wherein to receive the data to be sent comprises to receive one or more remote direct memory access (RDMA) commands comprising the data, wherein to compress the data comprises to compress the data of the one or more RDMA commands, wherein to send the compressed data to the one or more remote accelerator devices comprises to send the one or more RDMA commands with the compressed data of the RDMA commands to the one or more remote accelerator devices.

Example 4 includes the subject matter of any of Examples 1-3, and wherein the circuitry is further to receive telemetry data indicating a performance parameter of one or more remote accelerator devices; and determine, based on the telemetry data of the one or more remote accelerator devices, whether to compress the data received from the kernel, wherein to compress the data received from the kernel comprises to compress the data received from the kernel in response to a determination to compress the data based on the telemetry data of the one or more remote accelerator devices.

Example 5 includes the subject matter of any of Examples 1-4, and wherein the telemetry data of the one or more remote accelerator devices comprises an indication of available bandwidth between the accelerator device and the one or more remote accelerator devices, wherein to determine, based on the telemetry data of the one or more remote accelerator devices, whether to compress the data received from the kernel comprises to determine, based on the indication of available bandwidth between the accelerator device and the one or more remote accelerator devices, whether to compress the data received from the kernel.

Example 6 includes the subject matter of any of Examples 1-5, and wherein the circuitry is further to receive, from the kernel, an indication of a type of the data to be sent to the remote kernel; and select, based on the indication of the type of the data to be sent to the remote kernel, a compression algorithm, wherein to compress the data received from the kernel without instruction from the kernel comprises to compress the data received from the kernel with the compression algorithm selected based on the indication of the type of the data.

Example 7 includes a system comprising the accelerator device of claim 1, the system further comprising the compute device, the compute device comprising one or more machine-readable media comprising a plurality of instructions stored thereon that, when executed by the compute device, causes the compute device to determine, by an application of a processor of the compute device, the one or more parameters of the workload to be sent to the accelerator device; receive, by a network interface controller of the compute device and from the processor, the one or more parameters of the workload to be sent to the accelerator device; automatically compress, by the network interface controller and without instruction from the application, the one or more parameters of the workload; and send, by the network interface controller and to the accelerator device, the one or more compressed parameters of the workload.

Example 8 includes the subject matter of Example 7, and wherein to receive the one or more parameters to be sent comprises to receive one or more remote direct memory access (RDMA) commands comprising the one or more parameters of the workload, wherein to compress the data comprises to compress the one or more parameters of the workload, wherein to send the one or more compressed parameters to the accelerator device comprises to send the one or more RDMA commands with the one or more compressed parameters of the workload to the one or more remote accelerator devices.

Example 9 includes the subject matter of any of Examples 7 and 8, and wherein the plurality of instructions further cause the compute device to receive telemetry data indicating a performance parameter of the accelerator device; and determine, based on the telemetry data of the accelerator device, whether to compress the one or more parameters of the workload, wherein to compress the one or more parameters of the workload comprises to compress the one or more parameters of the workload in response to a determination to compress the one or more parameters of the workload based on the telemetry data of the accelerator device.

Example 10 includes a method comprising receiving, by circuitry of an accelerator device and from a compute device, one or more parameters of a workload, the workload to be performed by the accelerator device and one or more remote accelerator devices; providing, by the circuitry, the one or more parameters of the workload to a kernel of the accelerator device; receiving, by the circuitry and from the kernel, a workload result based on the one or more parameters of the workload; automatically compressing, by the circuitry, the workload result without instruction from the kernel; and sending, by the circuitry the compressed workload result to the compute device.

Example 11 includes the subject matter of Example 10, the method further comprising receiving, by the circuitry and from the kernel, data to be sent to a remote kernel on the one or more remote accelerator devices; compressing, by the circuitry, the data received from the kernel without instruction from the kernel; and sending, by the circuitry, the compressed data to the one or more remote accelerator devices.

Example 12 includes the subject matter of any of Examples 10 and 11, and wherein receiving the data to be sent comprises receiving one or more remote direct memory access (RDMA) commands comprising the data, wherein compressing the data comprises compressing the data of the one or more RDMA commands, wherein sending the compressed data to the one or more remote accelerator devices comprises sending the one or more RDMA commands with the compressed data of the RDMA commands to the one or more remote accelerator devices.

Example 13 includes the subject matter of any of Examples 10-12, the method further comprising receiving, by the circuitry, telemetry data indicating a performance parameter of one or more remote accelerator devices; and determining, by the circuitry and based on the telemetry data of the one or more remote accelerator devices, whether to compress the data received from the kernel, wherein compressing the data received from the kernel comprises compressing the data received from the kernel in response to a determination to compress the data based on the telemetry data of the one or more remote accelerator devices.

Example 14 includes the subject matter of any of Examples 10-13, and wherein the telemetry data of the one or more remote accelerator devices comprises an indication of available bandwidth between the accelerator device and the one or more remote accelerator devices, wherein determining, by the circuitry and based on the telemetry data of the one or more remote accelerator devices, whether to compress the data received from the kernel comprises determining, based on the indication of available bandwidth between the accelerator device and the one or more remote accelerator devices, whether to compress the data received from the kernel.

Example 15 includes the subject matter of any of Examples 10-14, the method further comprising determining, by an application of a processor of the compute device, the one or more parameters of the workload to be sent to the accelerator device; receiving, by a network interface controller of the compute device and from the processor, the one or more parameters of the workload to be sent to the accelerator device; automatically compressing, by the network interface controller and without instruction from the application, the one or more parameters of the workload; and sending, by the network interface controller and to the accelerator device, the one or more compressed parameters of the workload.

Example 16 includes the subject matter of any of Examples 10-15, and wherein receiving the one or more parameters to be sent comprises receiving one or more remote direct memory access (RDMA) commands comprising the one or more parameters of the workload, wherein compressing the data comprises compressing the one or more parameters of the workload, wherein sending the one or more compressed parameters to the accelerator device comprises sending the one or more RDMA commands with the one or more compressed parameters of the workload to the one or more remote accelerator devices.

Example 17 includes the subject matter of any of Examples 10-16, further comprising receiving, by the compute device, telemetry data indicating a performance parameter of the accelerator device; and determining, by the compute device and based on the telemetry data of the accelerator device, whether to compress the one or more parameters of the workload, wherein compressing the one or more parameters of the workload comprises compressing the one or more parameters of the workload in response to a determination to compress the one or more parameters of the workload based on the telemetry data of the accelerator device.

Example 18 includes one or more computer-readable media comprising a plurality of instructions stored thereon that, when executed, cause circuitry of an accelerator device to receive, from a compute device, one or more parameters of a workload, the workload to be performed by the accelerator device and one or more remote accelerator devices; provide the one or more parameters of the workload to a kernel of the accelerator device; receive, from the kernel, a workload result based on the one or more parameters of the workload; automatically compress the workload result without instruction from the kernel; and send the compressed workload result to the compute device.

Example 19 includes the subject matter of Example 18, and wherein the plurality of instructions further the circuitry to receive, from the kernel, data to be sent to a remote kernel on the one or more remote accelerator devices; compress the data received from the kernel without instruction from the kernel; and send the compressed data to the one or more remote accelerator devices.

Example 20 includes the subject matter of any of Examples 18 and 19, and wherein to receive the data to be sent comprises to receive one or more remote direct memory access (RDMA) commands comprising the data, wherein to compress the data comprises to compress the data of the one or more RDMA commands, wherein to send the compressed data to the one or more remote accelerator devices comprises to send the one or more RDMA commands with the compressed data of the RDMA commands to the one or more remote accelerator devices.

Example 21 includes the subject matter of any of Examples 18-20, and wherein the plurality of instructions further the circuitry to receive telemetry data indicating a performance parameter of one or more remote accelerator devices; and determine, based on the telemetry data of the one or more remote accelerator devices, whether to compress the data received from the kernel, wherein to compress the data received from the kernel comprises to compress the data received from the kernel in response to a determination to compress the data based on the telemetry data of the one or more remote accelerator devices.

Example 22 includes the subject matter of any of Examples 18-21, and wherein the telemetry data of the one or more remote accelerator devices comprises an indication of available bandwidth between the accelerator device and the one or more remote accelerator devices, wherein to determine, based on the telemetry data of the one or more remote accelerator devices, whether to compress the data received from the kernel comprises to determine, based on the indication of available bandwidth between the accelerator device and the one or more remote accelerator devices, whether to compress the data received from the kernel.

Example 23 includes the subject matter of any of Examples 18-22, and wherein the one or more machine-readable media comprising a second plurality of instructions stored thereon that, when executed by the compute device, causes the compute device to determine, by an application of a processor of the compute device, the one or more parameters of the workload to be sent to the accelerator device; receive, by a network interface controller of the compute device and from the processor, the one or more parameters of the workload to be sent to the accelerator device; automatically compress, by the network interface controller and without instruction from the application, the one or more parameters of the workload; and send, by the network interface controller and to the accelerator device, the one or more compressed parameters of the workload.

Example 24 includes the subject matter of any of Examples 18-23, and wherein to receive the one or more parameters to be sent comprises to receive one or more remote direct memory access (RDMA) commands comprising the one or more parameters of the workload, wherein to compress the data comprises to compress the one or more parameters of the workload, wherein to send the one or more compressed parameters to the accelerator device comprises to send the one or more RDMA commands with the one or more compressed parameters of the workload to the one or more remote accelerator devices.

Example 25 includes the subject matter of any of Examples 18-24, and wherein the second plurality of instructions further cause the compute device to receive telemetry data indicating a performance parameter of the accelerator device; and determine, based on the telemetry data of the accelerator device, whether to compress the one or more parameters of the workload, wherein to compress the one or more parameters of the workload comprises to compress the one or more parameters of the workload in response to a determination to compress the one or more parameters of the workload based on the telemetry data of the accelerator device. 

1. An accelerator device comprising: one or more slots; and circuitry to: receive, from a compute device, one or more parameters of a workload, the workload to be performed a slot of the one or more slots of the accelerator device and one or more slots of one or more remote accelerator devices; provide the one or more parameters of the workload to a kernel of the one or more slots of the accelerator device; receive, from the kernel, a workload result based on the one or more parameters of the workload; automatically compress the workload result without instruction from the kernel; and send the compressed workload result to the compute device.
 2. The accelerator device of claim 1, wherein the circuitry is further to: receive, from the kernel, data to be sent to a remote kernel on the one or more remote accelerator devices; compress the data received from the kernel without instruction from the kernel; and send the compressed data to the one or more remote accelerator devices.
 3. The accelerator device of claim 2, wherein to receive the data to be sent comprises to receive one or more remote direct memory access (RDMA) commands comprising the data, wherein to compress the data comprises to compress the data of the one or more RDMA commands, wherein to send the compressed data to the one or more remote accelerator devices comprises to send the one or more RDMA commands with the compressed data of the RDMA commands to the one or more remote accelerator devices.
 4. The accelerator device of claim 3, wherein the circuitry is further to: receive telemetry data indicating a performance parameter of one or more remote accelerator devices; and determine, based on the telemetry data of the one or more remote accelerator devices, whether to compress the data received from the kernel, wherein to compress the data received from the kernel comprises to compress the data received from the kernel in response to a determination to compress the data based on the telemetry data of the one or more remote accelerator devices.
 5. The accelerator device of claim 4, wherein the telemetry data of the one or more remote accelerator devices comprises an indication of available bandwidth between the accelerator device and the one or more remote accelerator devices, wherein to determine, based on the telemetry data of the one or more remote accelerator devices, whether to compress the data received from the kernel comprises to determine, based on the indication of available bandwidth between the accelerator device and the one or more remote accelerator devices, whether to compress the data received from the kernel.
 6. The accelerator device of claim 2, wherein the circuitry is further to: receive, from the kernel, an indication of a type of the data to be sent to the remote kernel; and select, based on the indication of the type of the data to be sent to the remote kernel, a compression algorithm, wherein to compress the data received from the kernel without instruction from the kernel comprises to compress the data received from the kernel with the compression algorithm selected based on the indication of the type of the data.
 7. A system comprising the accelerator device of claim 1, the system further comprising the compute device, the compute device comprising one or more machine-readable media comprising a plurality of instructions stored thereon that, when executed by the compute device, causes the compute device to: determine, by an application of a processor of the compute device, the one or more parameters of the workload to be sent to the accelerator device; receive, by a network interface controller of the compute device and from the processor, the one or more parameters of the workload to be sent to the accelerator device; automatically compress, by the network interface controller and without instruction from the application, the one or more parameters of the workload; and send, by the network interface controller and to the accelerator device, the one or more compressed parameters of the workload.
 8. The system of claim 7, wherein to receive the one or more parameters to be sent comprises to receive one or more remote direct memory access (RDMA) commands comprising the one or more parameters of the workload, wherein to compress the data comprises to compress the one or more parameters of the workload, wherein to send the one or more compressed parameters to the accelerator device comprises to send the one or more RDMA commands with the one or more compressed parameters of the workload to the one or more remote accelerator devices.
 9. The system of claim 8, wherein the plurality of instructions further cause the compute device to: receive telemetry data indicating a performance parameter of the accelerator device; and determine, based on the telemetry data of the accelerator device, whether to compress the one or more parameters of the workload, wherein to compress the one or more parameters of the workload comprises to compress the one or more parameters of the workload in response to a determination to compress the one or more parameters of the workload based on the telemetry data of the accelerator device.
 10. A method comprising: receiving, by circuitry of an accelerator device and from a compute device, one or more parameters of a workload, the workload to be performed by the accelerator device and one or more remote accelerator devices; providing, by the circuitry, the one or more parameters of the workload to a kernel of the accelerator device; receiving, by the circuitry and from the kernel, a workload result based on the one or more parameters of the workload; automatically compressing, by the circuitry, the workload result without instruction from the kernel; and sending, by the circuitry the compressed workload result to the compute device.
 11. The method of claim 10, the method further comprising: receiving, by the circuitry and from the kernel, data to be sent to a remote kernel on the one or more remote accelerator devices; compressing, by the circuitry, the data received from the kernel without instruction from the kernel; and sending, by the circuitry, the compressed data to the one or more remote accelerator devices.
 12. The method of claim 11, wherein receiving the data to be sent comprises receiving one or more remote direct memory access (RDMA) commands comprising the data, wherein compressing the data comprises compressing the data of the one or more RDMA commands, wherein sending the compressed data to the one or more remote accelerator devices comprises sending the one or more RDMA commands with the compressed data of the RDMA commands to the one or more remote accelerator devices.
 13. The method of claim 12, the method further comprising: receiving, by the circuitry, telemetry data indicating a performance parameter of one or more remote accelerator devices; and determining, by the circuitry and based on the telemetry data of the one or more remote accelerator devices, whether to compress the data received from the kernel, wherein compressing the data received from the kernel comprises compressing the data received from the kernel in response to a determination to compress the data based on the telemetry data of the one or more remote accelerator devices.
 14. The method of claim 13, wherein the telemetry data of the one or more remote accelerator devices comprises an indication of available bandwidth between the accelerator device and the one or more remote accelerator devices, wherein determining, by the circuitry and based on the telemetry data of the one or more remote accelerator devices, whether to compress the data received from the kernel comprises determining, based on the indication of available bandwidth between the accelerator device and the one or more remote accelerator devices, whether to compress the data received from the kernel.
 15. The method of claim 10, the method further comprising: determining, by an application of a processor of the compute device, the one or more parameters of the workload to be sent to the accelerator device; receiving, by a network interface controller of the compute device and from the processor, the one or more parameters of the workload to be sent to the accelerator device; automatically compressing, by the network interface controller and without instruction from the application, the one or more parameters of the workload; and sending, by the network interface controller and to the accelerator device, the one or more compressed parameters of the workload.
 16. The method of claim 15, wherein receiving the one or more parameters to be sent comprises receiving one or more remote direct memory access (RDMA) commands comprising the one or more parameters of the workload, wherein compressing the data comprises compressing the one or more parameters of the workload, wherein sending the one or more compressed parameters to the accelerator device comprises sending the one or more RDMA commands with the one or more compressed parameters of the workload to the one or more remote accelerator devices.
 17. The method of claim 16, the method further comprising: receiving, by the compute device, telemetry data indicating a performance parameter of the accelerator device; and determining, by the compute device and based on the telemetry data of the accelerator device, whether to compress the one or more parameters of the workload, wherein compressing the one or more parameters of the workload comprises compressing the one or more parameters of the workload in response to a determination to compress the one or more parameters of the workload based on the telemetry data of the accelerator device.
 18. One or more computer-readable media comprising a plurality of instructions stored thereon that, when executed, cause circuitry of an accelerator device to: receive, from a compute device, one or more parameters of a workload, the workload to be performed by the accelerator device and one or more remote accelerator devices; provide the one or more parameters of the workload to a kernel of the accelerator device; receive, from the kernel, a workload result based on the one or more parameters of the workload; automatically compress the workload result without instruction from the kernel; and send the compressed workload result to the compute device.
 19. The one or more computer-readable media of claim 18, wherein the plurality of instructions further the circuitry to: receive, from the kernel, data to be sent to a remote kernel on the one or more remote accelerator devices; compress the data received from the kernel without instruction from the kernel; and send the compressed data to the one or more remote accelerator devices.
 20. The one or more computer-readable media of claim 19, wherein to receive the data to be sent comprises to receive one or more remote direct memory access (RDMA) commands comprising the data, wherein to compress the data comprises to compress the data of the one or more RDMA commands, wherein to send the compressed data to the one or more remote accelerator devices comprises to send the one or more RDMA commands with the compressed data of the RDMA commands to the one or more remote accelerator devices.
 21. The one or more computer-readable media of claim 20, wherein the plurality of instructions further the circuitry to: receive telemetry data indicating a performance parameter of one or more remote accelerator devices; and determine, based on the telemetry data of the one or more remote accelerator devices, whether to compress the data received from the kernel, wherein to compress the data received from the kernel comprises to compress the data received from the kernel in response to a determination to compress the data based on the telemetry data of the one or more remote accelerator devices.
 22. The one or more computer-readable media of claim 21, wherein the telemetry data of the one or more remote accelerator devices comprises an indication of available bandwidth between the accelerator device and the one or more remote accelerator devices, wherein to determine, based on the telemetry data of the one or more remote accelerator devices, whether to compress the data received from the kernel comprises to determine, based on the indication of available bandwidth between the accelerator device and the one or more remote accelerator devices, whether to compress the data received from the kernel.
 23. The one or more computer-readable media of claim 18, one or more machine-readable media comprising a second plurality of instructions stored thereon that, when executed by the compute device, causes the compute device to: determine, by an application of a processor of the compute device, the one or more parameters of the workload to be sent to the accelerator device; receive, by a network interface controller of the compute device and from the processor, the one or more parameters of the workload to be sent to the accelerator device; automatically compress, by the network interface controller and without instruction from the application, the one or more parameters of the workload; and send, by the network interface controller and to the accelerator device, the one or more compressed parameters of the workload.
 24. The one or more computer-readable media of claim 23, wherein to receive the one or more parameters to be sent comprises to receive one or more remote direct memory access (RDMA) commands comprising the one or more parameters of the workload, wherein to compress the data comprises to compress the one or more parameters of the workload, wherein to send the one or more compressed parameters to the accelerator device comprises to send the one or more RDMA commands with the one or more compressed parameters of the workload to the one or more remote accelerator devices.
 25. The one or more computer-readable media of claim 24, wherein the second plurality of instructions further cause the compute device to: receive telemetry data indicating a performance parameter of the accelerator device; and determine, based on the telemetry data of the accelerator device, whether to compress the one or more parameters of the workload, wherein to compress the one or more parameters of the workload comprises to compress the one or more parameters of the workload in response to a determination to compress the one or more parameters of the workload based on the telemetry data of the accelerator device. 